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38,227,351 | A Novel Evaluation Model for Assessing ChatGPT on Otolaryngology-Head and Neck Surgery Certification Examinations: Performance Study. | Long C; Lowe K; Zhang J; Santos AD; Alanazi A; O'Brien D; Wright ED; Cote D | BACKGROUND: ChatGPT is among the most popular large language models (LLMs), exhibiting proficiency in various standardized tests, including multiple-choice medical board examinations. However, its performance on otolaryngology-head and neck surgery (OHNS) certification examinations and open-ended medical board certification examinations has not been reported. OBJECTIVE: We aimed to evaluate the performance of ChatGPT on OHNS board examinations and propose a novel method to assess an AI model's performance on open-ended medical board examination questions. METHODS: Twenty-one open-ended questions were adopted from the Royal College of Physicians and Surgeons of Canada's sample examination to query ChatGPT on April 11, 2023, with and without prompts. A new model, named Concordance, Validity, Safety, Competency (CVSC), was developed to evaluate its performance. RESULTS: In an open-ended question assessment, ChatGPT achieved a passing mark (an average of 75% across 3 trials) in the attempts and demonstrated higher accuracy with prompts. The model demonstrated high concordance (92.06%) and satisfactory validity. While demonstrating considerable consistency in regenerating answers, it often provided only partially correct responses. Notably, concerning features such as hallucinations and self-conflicting answers were observed. CONCLUSIONS: ChatGPT achieved a passing score in the sample examination and demonstrated the potential to pass the OHNS certification examination of the Royal College of Physicians and Surgeons of Canada. Some concerns remain due to its hallucinations, which could pose risks to patient safety. Further adjustments are necessary to yield safer and more accurate answers for clinical implementation. | Humans; Canada; Certification; Hallucinations; *Otolaryngology; *Surgeons | JMIR medical education | 2024 Jan 16 | 10.2196/49970 [doi] e49970 | https://pubmed.ncbi.nlm.nih.gov/38227351/ | not available | January 2024 |
38,276,235 | Applications of Artificial Intelligence in the Neuropsychological Assessment of Dementia: A Systematic Review. | Veneziani I; Marra A; Formica C; Grimaldi A; Marino S; Quartarone A; Maresca G | In the context of advancing healthcare, the diagnosis and treatment of cognitive disorders, particularly Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD), pose significant challenges. This review explores Artificial Intelligence (AI) and Machine Learning (ML) in neuropsychological assessment for the early detection and personalized treatment of MCI and AD. The review includes 37 articles that demonstrate that AI could be an useful instrument for optimizing diagnostic procedures, predicting cognitive decline, and outperforming traditional tests. Three main categories of applications are identified: (1) combining neuropsychological assessment with clinical data, (2) optimizing existing test batteries using ML techniques, and (3) employing virtual reality and games to overcome the limitations of traditional tests. Despite advancements, the review highlights a gap in developing tools that simplify the clinician's workflow and underscores the need for explainable AI in healthcare decision making. Future studies should bridge the gap between technical performance measures and practical clinical utility to yield accurate results and facilitate clinicians' roles. The successful integration of AI/ML in predicting dementia onset could reduce global healthcare costs and benefit aging societies. | null | Journal of personalized medicine | 2024 Jan 19 | 10.3390/jpm14010113 [doi] 113 | https://pubmed.ncbi.nlm.nih.gov/38276235/ | not available | January 2024 |
38,192,839 | Spatial-temporally and industrially heterogeneous effects of new infrastructure construction on fostering emerging industries in Chinese cities. | Xu J; Huang G; Ye Y; Liu Z | New infrastructure construction stemming from the new waves of technological revolution worldwide is exemplified by 5G base stations, big data centers, and ultra-high voltage. It has aroused extensive academic and policy interests in recent years, especially due to its beneficial role in empowering regional novel economic dynamics. However, this argument is still too general to capture the nuanced effects of new infrastructure construction on fostering emerging industries in specific spatial-temporal and industrial contexts, which is left for geographers to take up. This paper focuses on the spatial-temporally and industrially heterogeneous effects of new infrastructure construction on fostering four distinctive emerging industries in major Chinese cities over the last decade. It reveals that new infrastructure construction and emerging industries have experienced rapid development in major Chinese cities, with geographical agglomeration in national central cities with advanced economic development level. It is empirically demonstrated that new infrastructure construction can facilitate the development of emerging industries in major Chinese cities, while significant spatial-temporal heterogeneity characterizes the contributory forces. Furthermore, artificial intelligence as a Key Enabling Technology, robotics as a kind of hardware-featured industry, software-as-a-service as a software-centered industry, and blockchain as a networking-oriented industry vary markedly in the extent and the ways in which they benefit from new infrastructure construction, and they consequently exhibit industrial sensitivity to spatial-temporal heterogeneity in the fostering effects. | null | Heliyon | 2024 Jan 15 | 10.1016/j.heliyon.2023.e23774 [doi] e23774 | https://pubmed.ncbi.nlm.nih.gov/38192839/ | not available | January 2024 |
38,169,426 | Performance of the Winning Algorithms of the RSNA 2022 Cervical Spine Fracture Detection Challenge. | Lee GR; Flanders AE; Richards T; Kitamura F; Colak E; Lin HM; Ball RL; Talbott J; Prevedello LM | Purpose To evaluate and report the performance of the winning algorithms of the Radiological Society of North America Cervical Spine Fracture AI Challenge. Materials and Methods The competition was open to the public on Kaggle from July 28 to October 27, 2022. A sample of 3112 CT scans with and without cervical spine fractures (CSFx) were assembled from multiple sites (12 institutions across six continents) and prepared for the competition. The test set had 1093 scans (private test set: n = 789; mean age, 53.40 years +/- 22.86 [SD]; 509 males; public test set: n = 304; mean age, 52.51 years +/- 20.73; 189 males) and 847 fractures. The eight top-performing artificial intelligence (AI) algorithms were retrospectively evaluated, and the area under the receiver operating characteristic curve (AUC) value, F1 score, sensitivity, and specificity were calculated. Results A total of 1108 contestants composing 883 teams worldwide participated in the competition. The top eight AI models showed high performance, with a mean AUC value of 0.96 (95% CI: 0.95, 0.96), mean F1 score of 90% (95% CI: 90%, 91%), mean sensitivity of 88% (95% Cl: 86%, 90%), and mean specificity of 94% (95% CI: 93%, 96%). The highest values reported for previous models were an AUC of 0.85, F1 score of 81%, sensitivity of 76%, and specificity of 97%. Conclusion The competition successfully facilitated the development of AI models that could detect and localize CSFx on CT scans with high performance outcomes, which appear to exceed known values of previously reported models. Further study is needed to evaluate the generalizability of these models in a clinical environment. Keywords: Cervical Spine, Fracture Detection, Machine Learning, Artificial Intelligence Algorithms, CT, Head/Neck Supplemental material is available for this article. (c) RSNA, 2024. | Male; Humans; Middle Aged; Artificial Intelligence; Retrospective Studies; Algorithms; *Spinal Fractures/diagnosis; *Fractures, Bone; Cervical Vertebrae/diagnostic imaging | Radiology. Artificial intelligence | 2024 Jan | 10.1148/ryai.230256 [doi] e230256 | https://pubmed.ncbi.nlm.nih.gov/38169426/ | not available | January 2024 |
38,276,247 | Identifying and Exploring the Impact Factors for Intraocular Pressure Prediction in Myopic Children with Atropine Control Utilizing Multivariate Adaptive Regression Splines. | Wu TE; Chen JW; Liu TC; Yu CH; Jhou MJ; Lu CJ | PURPOSE: The treatment of childhood myopia often involves the use of topical atropine, which has been demonstrated to be effective in decelerating the progression of myopia. It is crucial to monitor intraocular pressure (IOP) to ensure the safety of topical atropine. This study aims to identify the optimal machine learning IOP-monitoring module and establish a precise baseline IOP as a clinical safety reference for atropine medication. METHODS: Data from 1545 eyes of 1171 children receiving atropine for myopia were retrospectively analyzed. Nineteen variables including patient demographics, medical history, refractive error, and IOP measurements were considered. The data were analyzed using a multivariate adaptive regression spline (MARS) model to analyze the impact of different factors on the End IOP. RESULTS: The MARS model identified age, baseline IOP, End Spherical, duration of previous atropine treatment, and duration of current atropine treatment as the five most significant factors influencing the End IOP. The outcomes revealed that the baseline IOP had the most significant effect on final IOP, exhibiting a notable knot at 14 mmHg. When the baseline IOP was equal to or exceeded 14 mmHg, there was a positive correlation between atropine use and End IOP, suggesting that atropine may increase the End IOP in children with a baseline IOP greater than 14 mmHg. CONCLUSIONS: MARS model demonstrates a better ability to capture nonlinearity than classic multiple linear regression for predicting End IOP. It is crucial to acknowledge that administrating atropine may elevate intraocular pressure when the baseline IOP exceeds 14 mmHg. These findings offer valuable insights into factors affecting IOP in children undergoing atropine treatment for myopia, enabling clinicians to make informed decisions regarding treatment options. | null | Journal of personalized medicine | 2024 Jan 22 | 10.3390/jpm14010125 [doi] 125 | https://pubmed.ncbi.nlm.nih.gov/38276247/ | not available | January 2024 |
38,918,352 | Cognition of Time and Thinking Beyond. | Bi Z | A common research protocol in cognitive neuroscience is to train subjects to perform deliberately designed experiments while recording brain activity, with the aim of understanding the brain mechanisms underlying cognition. However, how the results of this protocol of research can be applied in technology is seldom discussed. Here, I review the studies on time processing of the brain as examples of this research protocol, as well as two main application areas of neuroscience (neuroengineering and brain-inspired artificial intelligence). Time processing is a fundamental dimension of cognition, and time is also an indispensable dimension of any real-world signal to be processed in technology. Therefore, one may expect that the studies of time processing in cognition profoundly influence brain-related technology. Surprisingly, I found that the results from cognitive studies on timing processing are hardly helpful in solving practical problems. This awkward situation may be due to the lack of generalizability of the results of cognitive studies, which are under well-controlled laboratory conditions, to real-life situations. This lack of generalizability may be rooted in the fundamental unknowability of the world (including cognition). Overall, this paper questions and criticizes the usefulness and prospect of the abovementioned research protocol of cognitive neuroscience. I then give three suggestions for future research. First, to improve the generalizability of research, it is better to study brain activity under real-life conditions instead of in well-controlled laboratory experiments. Second, to overcome the unknowability of the world, we can engineer an easily accessible surrogate of the object under investigation, so that we can predict the behavior of the object under investigation by experimenting on the surrogate. Third, the paper calls for technology-oriented research, with the aim of technology creation instead of knowledge discovery. | Humans; *Cognition/physiology; *Brain/physiology; *Thinking/physiology; Cognitive Neuroscience/methods; Artificial Intelligence; Time Perception/physiology | Advances in experimental medicine and biology | 2024 | 10.1007/978-3-031-60183-5_10 [doi] | https://pubmed.ncbi.nlm.nih.gov/38918352/ | not available | January 2024 |
38,126,237 | Deep Learning and Biased Prediction: More Questions Than Answers? | Rosenberg MA | null | Humans; *Deep Learning; *Heart Failure; Machine Learning | Circulation. Heart failure | 2024 Jan | 10.1161/CIRCHEARTFAILURE.123.011368 [doi] | https://pubmed.ncbi.nlm.nih.gov/38126237/ | not available | January 2024 |
38,291,419 | Nomogram for hospital-acquired venous thromboembolism among patients with cardiovascular diseases. | Luo Q; Li X; Zhao Z; Zhao Q; Liu Z; Yang W | BACKGROUND: Identifying venous thromboembolism (VTE) is challenging for patients with cardiovascular diseases due to similar clinical presentation. Most hospital-acquired VTE events are preventable, whereas the implementation of VTE prophylaxis in clinical practice is far from sufficient. There is a lack of hospital-acquired VTE prediction models tailored specifically designed for patients with cardiovascular diseases. We aimed to develop a nomogram predicting hospital-acquired VTE specifically for patients with cardiovascular diseases. MATERIAL AND METHODS: Consecutive patients with cardiovascular diseases admitted to internal medicine of Fuwai hospital between September 2020 and August 2021 were included. Univariable and multivariable logistic regression were applied to identify risk factors of hospital-acquired VTE. A nomogram was constructed according to multivariable logistic regression, and internally validated by bootstrapping. RESULTS: A total of 27,235 patients were included. During a median hospitalization of four days, 154 (0.57%) patients developed hospital-acquired VTE. Multivariable logistic regression identified that female sex, age, infection, pulmonary hypertension, obstructive sleep apnea, acute coronary syndrome, cardiomyopathy, heart failure, immobility, central venous catheter, intra-aortic balloon pump and anticoagulation were independently associated with hospital-acquired VTE. The nomogram was constructed with high accuracy in both the training set and validation (concordance index 0.865 in the training set, and 0.864 in validation), which was further confirmed in calibration. Compared to Padua model, the Fuwai model demonstrated significantly better discrimination ability (area under curve 0.865 vs. 0.786, net reclassification index 0.052, 95% confidence interval 0.012-0.091, P = 0.009; integrated discrimination index 0.020, 95% confidence interval 0.001-0.039, P = 0.051). CONCLUSION: The incidence of hospital-acquired VTE in patients with cardiovascular diseases is relatively low. The nomogram exhibits high accuracy in predicting hospital-acquired VTE in patients with cardiovascular diseases. | null | Thrombosis journal | 2024 Jan 30 | 10.1186/s12959-024-00584-w [doi] 15 | https://pubmed.ncbi.nlm.nih.gov/38291419/ | not available | January 2024 |
38,509,680 | Identification of Drug Targets and Agents Associated with Ferroptosis-related Osteoporosis through Integrated Network Pharmacology and Molecular Docking Technology. | Huo K; Yang Y; Yang T; Zhang W; Shao J | BACKGROUND: Osteoporosis is a systemic bone disease characterized by progressive reduction of bone mineral density and degradation of trabecular bone microstructure. Iron metabolism plays an important role in bone; its imbalance leads to abnormal lipid oxidation in cells, hence ferroptosis. In osteoporosis, however, the exact mechanism of ferroptosis has not been fully elucidated. OBJECTIVE: The main objective of this project was to identify potential drug target proteins and agents for the treatment of ferroptosis-related osteoporosis. METHODS: In the current study, we investigated the differences in gene expression of bone marrow mesenchymal stem cells between osteoporosis patients and normal individuals using bioinformatics methods to obtain ferroptosis-related genes. We could predict their protein structure based on the artificial intelligence database of AlphaFold, and their target drugs and binding sites with the network pharmacology and molecular docking technology. RESULTS: We identified five genes that were highly associated with osteoporosis, such as TP53, EGFR, TGFB1, SOX2 and MAPK14, which, we believe, can be taken as the potential markers and targets for the diagnosis and treatment of osteoporosis. Furthermore, we observed that these five genes were highly targeted by resveratrol to exert a therapeutic effect on ferroptosis-related osteoporosis. CONCLUSION: We examined the relationship between ferroptosis and osteoporosis based on bioinformatics and network pharmacology, presenting a promising direction to the pursuit of the exact molecular mechanism of osteoporosis so that a new target can be discovered for the treatment of osteoporosis. | *Ferroptosis/drug effects; *Osteoporosis/drug therapy/metabolism; Humans; *Molecular Docking Simulation; *Network Pharmacology; Mesenchymal Stem Cells/drug effects/metabolism; Computational Biology; Resveratrol/pharmacology/chemistry | Current pharmaceutical design | 2024 | 10.2174/0113816128288225240318045050 [doi] | https://pubmed.ncbi.nlm.nih.gov/38509680/ | not available | January 2024 |
38,254,241 | Antimicrobial resistance crisis: could artificial intelligence be the solution? | Liu GY; Yu D; Fan MM; Zhang X; Jin ZY; Tang C; Liu XF | Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed. The discovery and introduction of novel antibiotics are time-consuming and expensive. According to WHO's report of antibacterial agents in clinical development, only 18 novel antibiotics have been approved since 2014. Therefore, novel antibiotics are critically needed. Artificial intelligence (AI) has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics. Here, we first summarized recently marketed novel antibiotics, and antibiotic candidates in clinical development. In addition, we systematically reviewed the involvement of AI in antibacterial drug development and utilization, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well as resistance mechanism prediction, and antibiotic stewardship. | Humans; *Artificial Intelligence; *Anti-Bacterial Agents/pharmacology/therapeutic use; Drug Resistance, Bacterial; Public Health | Military Medical Research | 2024 Jan 23 | 10.1186/s40779-024-00510-1 [doi] 7 | https://pubmed.ncbi.nlm.nih.gov/38254241/ | not available | January 2024 |
38,168,953 | The pathophysiology of sepsis and precision-medicine-based immunotherapy. | Giamarellos-Bourboulis EJ; Aschenbrenner AC; Bauer M; Bock C; Calandra T; Gat-Viks I; Kyriazopoulou E; Lupse M; Monneret G; Pickkers P; Schultze JL; van der Poll T; van de Veerdonk FL; Vlaar APJ; Weis S; Wiersinga WJ; Netea MG | Sepsis remains a major cause of morbidity and mortality in both low- and high-income countries. Antibiotic therapy and supportive care have significantly improved survival following sepsis in the twentieth century, but further progress has been challenging. Immunotherapy trials for sepsis, mainly aimed at suppressing the immune response, from the 1990s and 2000s, have largely failed, in part owing to unresolved patient heterogeneity in the underlying immune disbalance. The past decade has brought the promise to break this blockade through technological developments based on omics-based technologies and systems medicine that can provide a much larger data space to describe in greater detail the immune endotypes in sepsis. Patient stratification opens new avenues towards precision medicine approaches that aim to apply immunotherapies to sepsis, on the basis of precise biomarkers and molecular mechanisms defining specific immune endotypes. This approach has the potential to lead to the establishment of immunotherapy as a successful pillar in the treatment of sepsis for future generations. | Humans; *Precision Medicine; *Sepsis/therapy; Immunotherapy; Biomarkers | Nature immunology | 2024 Jan | 10.1038/s41590-023-01660-5 [doi] | https://pubmed.ncbi.nlm.nih.gov/38168953/ | not available | January 2024 |
38,256,066 | Harnessing the Stem Cell Niche in Regenerative Medicine: Innovative Avenue to Combat Neurodegenerative Diseases. | Velikic G; Maric DM; Maric DL; Supic G; Puletic M; Dulic O; Vojvodic D | Regenerative medicine harnesses the body's innate capacity for self-repair to restore malfunctioning tissues and organs. Stem cell therapies represent a key regenerative strategy, but to effectively harness their potential necessitates a nuanced understanding of the stem cell niche. This specialized microenvironment regulates critical stem cell behaviors including quiescence, activation, differentiation, and homing. Emerging research reveals that dysfunction within endogenous neural stem cell niches contributes to neurodegenerative pathologies and impedes regeneration. Strategies such as modifying signaling pathways, or epigenetic interventions to restore niche homeostasis and signaling, hold promise for revitalizing neurogenesis and neural repair in diseases like Alzheimer's and Parkinson's. Comparative studies of highly regenerative species provide evolutionary clues into niche-mediated renewal mechanisms. Leveraging endogenous bioelectric cues and crosstalk between gut, brain, and vascular niches further illuminates promising therapeutic opportunities. Emerging techniques like single-cell transcriptomics, organoids, microfluidics, artificial intelligence, in silico modeling, and transdifferentiation will continue to unravel niche complexity. By providing a comprehensive synthesis integrating diverse views on niche components, developmental transitions, and dynamics, this review unveils new layers of complexity integral to niche behavior and function, which unveil novel prospects to modulate niche function and provide revolutionary treatments for neurodegenerative diseases. | Humans; *Regenerative Medicine; Artificial Intelligence; *Neurodegenerative Diseases/therapy; Stem Cell Niche; Biological Evolution | International journal of molecular sciences | 2024 Jan 12 | 10.3390/ijms25020993 [doi] 993 | https://pubmed.ncbi.nlm.nih.gov/38256066/ | not available | January 2024 |
38,165,624 | Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature. | Susnjak T | This chapter presents a practical guide for conducting sentiment analysis using Natural Language Processing (NLP) techniques in the domain of tick-borne disease text. The aim is to demonstrate the process of how the presence of bias in the discourse surrounding chronic manifestations of the disease can be evaluated. The goal is to use a dataset of 5643 abstracts collected from scientific journals on the topic of chronic Lyme disease to demonstrate using Python, the steps for conducting sentiment analysis using pretrained language models and the process of validating the preliminary results using both interpretable machine learning tools, as well as a novel methodology of leveraging emerging state-of-the-art large language models like ChatGPT. This serves as a useful resource for researchers and practitioners interested in using NLP techniques for sentiment analysis in the medical domain. | Humans; *Sentiment Analysis; *Lyme Disease; Publications; Language; Machine Learning | Methods in molecular biology (Clifton, N.J.) | 2024 | 10.1007/978-1-0716-3561-2_14 [doi] | https://pubmed.ncbi.nlm.nih.gov/38165624/ | not available | January 2024 |
37,951,600 | Evaluation of objective tools and artificial intelligence in robotic surgery technical skills assessment: a systematic review. | Boal MWE; Anastasiou D; Tesfai F; Ghamrawi W; Mazomenos E; Curtis N; Collins JW; Sridhar A; Kelly J; Stoyanov D; Francis NK | BACKGROUND: There is a need to standardize training in robotic surgery, including objective assessment for accreditation. This systematic review aimed to identify objective tools for technical skills assessment, providing evaluation statuses to guide research and inform implementation into training curricula. METHODS: A systematic literature search was conducted in accordance with the PRISMA guidelines. Ovid Embase/Medline, PubMed and Web of Science were searched. Inclusion criterion: robotic surgery technical skills tools. Exclusion criteria: non-technical, laparoscopy or open skills only. Manual tools and automated performance metrics (APMs) were analysed using Messick's concept of validity and the Oxford Centre of Evidence-Based Medicine (OCEBM) Levels of Evidence and Recommendation (LoR). A bespoke tool analysed artificial intelligence (AI) studies. The Modified Downs-Black checklist was used to assess risk of bias. RESULTS: Two hundred and forty-seven studies were analysed, identifying: 8 global rating scales, 26 procedure-/task-specific tools, 3 main error-based methods, 10 simulators, 28 studies analysing APMs and 53 AI studies. Global Evaluative Assessment of Robotic Skills and the da Vinci Skills Simulator were the most evaluated tools at LoR 1 (OCEBM). Three procedure-specific tools, 3 error-based methods and 1 non-simulator APMs reached LoR 2. AI models estimated outcomes (skill or clinical), demonstrating superior accuracy rates in the laboratory with 60 per cent of methods reporting accuracies over 90 per cent, compared to real surgery ranging from 67 to 100 per cent. CONCLUSIONS: Manual and automated assessment tools for robotic surgery are not well validated and require further evaluation before use in accreditation processes.PROSPERO: registration ID CRD42022304901. | Humans; *Robotic Surgical Procedures/education; Artificial Intelligence; Clinical Competence; *Robotics; *Laparoscopy/education | The British journal of surgery | 2024 Jan 3 | 10.1093/bjs/znad331 [doi] znad331 | https://pubmed.ncbi.nlm.nih.gov/37951600/ | not available | January 2024 |
38,820,039 | Left atrial stiffness is in correlation with left atrial reservoir strain in pediatric patients with mitral regurgitation. | Begic Z; Djukic M; Begic E; Aziri B; Gojak R; Mladenovic Z; Begic N; Badnjevic A | BACKGROUND: Left atrial stiffness index (LASI), defined as the ratio of early diastolic transmitral flow velocity/lateral mitral annulus myocardial velocity (E/e') to peak atrial strain, reflects reduced left atrial (LA) compliance and represents an emerging marker that can be used for noninvasive measurement of fibrosis of LA in patients with mitral regurgitation (MR). OBJECTIVE: To investigate the impact of chronic MR in children and adolescents on the remodeling and function of the LA, quantified through strain parameters and diastolic function. METHODS: The study included fifty patients (n= 50) diagnosed with primary and secondary chronic MR lasting at least 5 years. The echocardiographic recordings were performed by a third party, two cardiologists actively engaged in echocardiography on a daily basis. RESULTS: Older participants had higher values of the LASI (r= 0.467, p= 0.001). Participants with higher LASI values had a smaller LA reservoir (r= 0.784, p= 0.0001) and smaller LA conduit values (r=-0.374, p= 0.00). Participants with higher LASI values had a larger LA diameter (r= 0.444, p-value= 0.001) and higher average E/e' ratio (r= 0.718, p= 0.0001). There was a significant difference (p= 0.04) in the LASI among participants based on the MR jet area (< 20.85% ⩾ 20.85%), LASI was higher in participants with an area greater than 20.85%. Differences in other parameters such as LA reservoir, LA conduit, LA contractile were not statistically significant. CONCLUSION: Increased LA stiffness is associated with diminished atrial compliance and reservoir capacity, and LASI has a potential to as an early marker for assessing disease severity and progression in pediatric MR. | Humans; *Mitral Valve Insufficiency/physiopathology/diagnostic imaging; Female; Male; Child; Adolescent; *Heart Atria/physiopathology/diagnostic imaging; *Atrial Function, Left/physiology; Echocardiography/methods | Technology and health care : official journal of the European Society for Engineering and Medicine | 2024 | 10.3233/THC-240402 [doi] | https://pubmed.ncbi.nlm.nih.gov/38820039/ | not available | January 2024 |
38,087,950 | Functional network dynamics revealed by EEG microstates reflect cognitive decline in amyotrophic lateral sclerosis. | Metzger M; Dukic S; McMackin R; Giglia E; Mitchell M; Bista S; Costello E; Peelo C; Tadjine Y; Sirenko V; Plaitano S; Coffey A; McManus L; Farnell Sharp A; Mehra P; Heverin M; Bede P; Muthuraman M; Pender N; Hardiman O; Nasseroleslami B | Recent electroencephalography (EEG) studies have shown that patterns of brain activity can be used to differentiate amyotrophic lateral sclerosis (ALS) and control groups. These differences can be interrogated by examining EEG microstates, which are distinct, reoccurring topographies of the scalp's electrical potentials. Quantifying the temporal properties of the four canonical microstates can elucidate how the dynamics of functional brain networks are altered in neurological conditions. Here we have analysed the properties of microstates to detect and quantify signal-based abnormality in ALS. High-density resting-state EEG data from 129 people with ALS and 78 HC were recorded longitudinally over a 24-month period. EEG topographies were extracted at instances of peak global field power to identify four microstate classes (labelled A-D) using K-means clustering. Each EEG topography was retrospectively associated with a microstate class based on global map dissimilarity. Changes in microstate properties over the course of the disease were assessed in people with ALS and compared with changes in clinical scores. The topographies of microstate classes remained consistent across participants and conditions. Differences were observed in coverage, occurrence, duration, and transition probabilities between ALS and control groups. The duration of microstate class B and coverage of microstate class C correlated with lower limb functional decline. The transition probabilities A to D, C to B and C to B also correlated with cognitive decline (total ECAS) in those with cognitive and behavioural impairments. Microstate characteristics also significantly changed over the course of the disease. Examining the temporal dependencies in the sequences of microstates revealed that the symmetry and stationarity of transition matrices were increased in people with late-stage ALS. These alterations in the properties of EEG microstates in ALS may reflect abnormalities within the sensory network and higher-order networks. Microstate properties could also prospectively predict symptom progression in those with cognitive impairments. | Humans; *Amyotrophic Lateral Sclerosis; Electroencephalography; Retrospective Studies; Brain; Brain Mapping; *Cognitive Dysfunction/etiology | Human brain mapping | 2024 Jan | 10.1002/hbm.26536 [doi] e26536 | https://pubmed.ncbi.nlm.nih.gov/38087950/ | not available | January 2024 |
38,129,616 | AI consciousness: scientists say we urgently need answers. | Lenharo M | null | Humans; *Artificial Intelligence; *Consciousness; *Research/economics/trends; Research Personnel; *Research Support as Topic/economics/trends | Nature | 2024 Jan | 10.1038/d41586-023-04047-6 [doi] | https://pubmed.ncbi.nlm.nih.gov/38129616/ | not available | January 2024 |
38,303,466 | Self-adaptive attention fusion for multimodal aspect-based sentiment analysis. | Wang Z; Guo J | Multimodal aspect term extraction (MATE) and multimodal aspect-oriented sentiment classification (MASC) are two crucial subtasks in multimodal sentiment analysis. The use of pretrained generative models has attracted increasing attention in aspect-based sentiment analysis (ABSA). However, the inherent semantic gap between textual and visual modalities poses a challenge in transferring text-based generative pretraining models to image-text multimodal sentiment analysis tasks. To tackle this issue, this paper proposes a self-adaptive cross-modal attention fusion architecture for joint multimodal aspect-based sentiment analysis (JMABSA), which is a generative model based on an image-text selective fusion mechanism that aims to bridge the semantic gap between text and image representations and adaptively transfer a textual-based pretraining model to the multimodal JMASA task. We conducted extensive experiments on two benchmark datasets, and the experimental results show that our model significantly outperforms other state of the art approaches by a significant margin. | null | Mathematical biosciences and engineering : MBE | 2024 Jan | 10.3934/mbe.2024056 [doi] | https://pubmed.ncbi.nlm.nih.gov/38303466/ | not available | January 2024 |
38,180,783 | Application of eHealth Tools in Anticoagulation Management After Cardiac Valve Replacement: Scoping Review Coupled With Bibliometric Analysis. | Wu Y; Wang X; Zhou M; Huang Z; Liu L; Cong L | BACKGROUND: Anticoagulation management can effectively prevent complications in patients undergoing cardiac valve replacement (CVR). The emergence of eHealth tools provides new prospects for the management of long-term anticoagulants. However, there is no comprehensive summary of the application of eHealth tools in anticoagulation management after CVR. OBJECTIVE: Our objective is to clarify the current state, trends, benefits, and challenges of using eHealth tools in the anticoagulation management of patients after CVR and provide future directions and recommendations for development in this field. METHODS: This scoping review follows the 5-step framework developed by Arksey and O'Malley. We searched 5 databases such as PubMed, MEDLINE, Web of Science, CINAHL, and Embase using keywords such as "eHealth," "anticoagulation," and "valve replacement." We included papers on the practical application of eHealth tools and excluded papers describing the underlying mechanisms for developing eHealth tools. The search time ranged from the database inception to March 1, 2023. The study findings were reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Additionally, VOSviewer (version 1.6.18) was used to construct visualization maps of countries, institutions, authors, and keywords to investigate the internal relations of included literature and to explore research hotspots and frontiers. RESULTS: This study included 25 studies that fulfilled the criteria. There were 27,050 participants in total, with the sample size of the included studies ranging from 49 to 13,219. The eHealth tools mainly include computer-based support systems, electronic health records, telemedicine platforms, and mobile apps. Compared to traditional anticoagulation management, eHealth tools can improve time in therapeutic range and life satisfaction. However, there is no significant impact observed in terms of economic benefits and anticoagulation-related complications. Bibliometric analysis suggests the potential for increased collaboration and opportunities among countries and academic institutions. Italy had the widest cooperative relationships. Machine learning and artificial intelligence are the popular research directions in anticoagulation management. CONCLUSIONS: eHealth tools exhibit promise for clinical applications in anticoagulation management after CVR, with the potential to enhance postoperative rehabilitation. Further high-quality research is needed to explore the economic benefits of eHealth tools in long-term anticoagulant therapy and the potential to reduce the occurrence of adverse events. | Humans; *Artificial Intelligence; *Bibliometrics; Anticoagulants/therapeutic use; Computer Systems; Heart Valves | JMIR mHealth and uHealth | 2024 Jan 5 | 10.2196/48716 [doi] e48716 | https://pubmed.ncbi.nlm.nih.gov/38180783/ | not available | January 2024 |
38,021,090 | A feasibility trial of skin surface motion-gated stereotactic body radiotherapy for treatment of upper abdominal or lower thoracic targets using a novel O-ring gantry. | Kiser K; Schiff J; Laugeman E; Kim T; Green O; Hatscher C; Kim H; Badiyan S; Spraker M; Samson P; Robinson C; Price A; Henke L | BACKGROUND AND PURPOSE: A novel O-ring gantry can deliver stereotactic body radiation therapy (SBRT) with artificial intelligence-facilitated, CT-guided online plan adaptation. It gates mobile targets by optically monitoring skin surface motion. However, this gating solution has not been clinically validated. We conducted a trial to evaluate the feasibility of optical skin surface-guided gating for patients with mobile upper abdominal or lower thoracic malignancies treated with SBRT on this platform (NCT05030454). MATERIALS AND METHODS: Ten patients who were prescribed SBRT to a thoracic or abdominal target and were capable of breath-hold for at least 17 s enrolled. They received SBRT in five fractions with breath-hold technique and optical skin surface motion monitored-gating with a +/- 2 mm tolerance. Online plan adaptation was left to the discretion of the daily treating physician. The primary endpoint was defined as successful completion of > 75 % of attempted fractions. Exploratory endpoints included local control and acute grade >/= 3 toxicity rates after three months. For adapted fractions the contouring, planning, quality assurance, and treatment delivery times were recorded. RESULTS: Forty-seven of 51 SBRT fractions (92 %) were successfully gated at breath-hold by optical skin surface motion monitoring. The tumor centroid position during breath-hold varied by a mean of approximately 2 mm. Sixty-three percent of fractions were adapted online with a median total treatment time of 78.5 min. After three months no local recurrences or acute grade >/= 3 toxicities were observed. CONCLUSIONS: SBRT treatment to mobile targets with surface-monitored gating on a novel O-ring gantry was prospectively validated. | null | Clinical and translational radiation oncology | 2024 Jan | 10.1016/j.ctro.2023.100692 [doi] 100692 | https://pubmed.ncbi.nlm.nih.gov/38021090/ | not available | January 2024 |
38,234,256 | Fast and accurate excited states predictions: machine learning and diabatization. | Srsen S; von Lilienfeld OA; Slavicek P | The efficiency of machine learning algorithms for electronically excited states is far behind ground-state applications. One of the underlying problems is the insufficient smoothness of the fitted potential energy surfaces and other properties in the vicinity of state crossings and conical intersections, which is a prerequisite for an efficient regression. Smooth surfaces can be obtained by switching to the diabatic basis. However, diabatization itself is still an outstanding problem. We overcome these limitations by solving both problems at once. We use a machine learning approach combining clustering and regression techniques to correct for the deficiencies of property-based diabatization which, in return, provides us with smooth surfaces that can be easily fitted. Our approach extends the applicability of property-based diabatization to multidimensional systems. We utilize the proposed diabatization scheme to achieve higher prediction accuracy for adiabatic states and we show its performance by reconstructing global potential energy surfaces of excited states of nitrosyl fluoride and formaldehyde. While the proposed methodology is independent of the specific property-based diabatization and regression algorithm, we show its performance for kernel ridge regression and a very simple diabatization based on transition multipoles. Compared to most other algorithms based on machine learning, our approach needs only a small amount of training data. | null | Physical chemistry chemical physics : PCCP | 2024 Jan 31 | 10.1039/d3cp05685f [doi] | https://pubmed.ncbi.nlm.nih.gov/38234256/ | not available | January 2024 |
38,170,492 | How to Navigate the Pitfalls of AI Hype in Health Care. | Suran M; Hswen Y | null | *Artificial Intelligence; *Delivery of Health Care/methods/standards; Health Facilities | JAMA | 2024 Jan 23 | 10.1001/jama.2023.23330 [doi] | https://pubmed.ncbi.nlm.nih.gov/38170492/ | not available | January 2024 |
40,417,470 | LLMs-based Few-Shot Disease Predictions using EHR: A Novel Approach Combining Predictive Agent Reasoning and Critical Agent Instruction. | Cui H; Shen Z; Zhang J; Shao H; Qin L; Ho JC; Yang C | Electronic health records (EHRs) contain valuable patient data for health-related prediction tasks, such as disease prediction. Traditional approaches rely on supervised learning methods that require large labeled datasets, which can be expensive and challenging to obtain. In this study, we investigate the feasibility of applying Large Language Models (LLMs) to convert structured patient visit data (e.g., diagnoses, labs, prescriptions) into natural language narratives. We evaluate the zero-shot and few-shot performance of LLMs using various EHR-prediction-oriented prompting strategies. Furthermore, we propose a novel approach that utilizes LLM agents with different roles: a predictor agent that makes predictions and generates reasoning processes and a critic agent that analyzes incorrect predictions and provides guidance for improving the reasoning of the predictor agent. Our results demonstrate that with the proposed approach, LLMs can achieve decent few-shot performance compared to traditional supervised learning methods in EHR-based disease predictions, suggesting its potential for health-oriented applications. | *Electronic Health Records; *Natural Language Processing; Humans; Machine Learning | AMIA ... Annual Symposium proceedings. AMIA Symposium | 2024 | null | https://pubmed.ncbi.nlm.nih.gov/40417470/ | not available | January 2024 |
38,253,758 | Pneumonia detection based on RSNA dataset and anchor-free deep learning detector. | Wu L; Zhang J; Wang Y; Ding R; Cao Y; Liu G; Liufu C; Xie B; Kang S; Liu R; Li W; Guan F | Pneumonia is a highly lethal disease, and research on its treatment and early screening tools has received extensive attention from researchers. Due to the maturity and cost reduction of chest X-ray technology, and with the development of artificial intelligence technology, pneumonia identification based on deep learning and chest X-ray has attracted attention from all over the world. Although the feature extraction capability of deep learning is strong, existing deep learning object detection frameworks are based on pre-defined anchors, which require a lot of tuning and experience to guarantee their excellent results in the face of new applications or data. To avoid the influence of anchor settings in pneumonia detection, this paper proposes an anchor-free object detection framework and RSNA dataset based on pneumonia detection. First, a data enhancement scheme is used to preprocess the chest X-ray images; second, an anchor-free object detection framework is used for pneumonia detection, which contains a feature pyramid, two-branch detection head, and focal loss. The average precision of 51.5 obtained by Intersection over Union (IoU) calculation shows that the pneumonia detection results obtained in this paper can surpass the existing classical object detection framework, providing an idea for future research and exploration. | Humans; Artificial Intelligence; *Deep Learning; *Pneumonia/diagnostic imaging; Pyramidal Tracts; Research Personnel | Scientific reports | 2024 Jan 22 | 10.1038/s41598-024-52156-7 [doi] 1929 | https://pubmed.ncbi.nlm.nih.gov/38253758/ | not available | January 2024 |
38,359,852 | Fostering Digital Health in Universities: An Experience of the First Junior Scientific Committee of the Brazilian Congress of Health Informatics. | Pantaleao AN; Mennitti AL; Brunheroto FB; Stavis V; Ricoboni LT; Castro VAF; Ferreira OF; Lage EM; Carvalho DR; Fernandes AMDR; Gaspar JS | OBJECTIVES: Digital health (DH) is a revolution driven by digital technologies to improve health. Despite the importance of DH, curricular updates in healthcare university programs are scarce, and DH remains undervalued. Therefore, this report describes the first Junior Scientific Committee (JSC) focusing on DH at a nationwide congress, with the aim of affirming its importance for promoting DH in universities. METHODS: The scientific committee of the Brazilian Congress of Health Informatics (CBIS) extended invitations to students engaged in health-related fields, who were tasked with organizing a warm-up event and a 4-hour session at CBIS. Additionally, they were encouraged to take an active role in a workshop alongside distinguished experts to map out the current state of DH in Brazil. RESULTS: The warm-up event focused on the topic "Artificial intelligence in healthcare: is a new concept of health about to arise?" and featured remote discussions by three professionals from diverse disciplines. At CBIS, the JSC's inaugural presentation concentrated on delineating the present state of DH education in Brazil, while the second presentation offered strategies to advance DH, incorporating viewpoints from within and beyond the academic sphere. During the workshop, participants deliberated on the most crucial competencies for future professionals in the DH domain. CONCLUSIONS: Forming a JSC proved to be a valuable tool to foster DH, particularly due to the valuable interactions it facilitated between esteemed professionals and students. It also supports the cultivation of leadership skills in DH, a field that has not yet received the recognition it deserves. | null | Healthcare informatics research | 2024 Jan | 10.4258/hir.2024.30.1.83 [doi] | https://pubmed.ncbi.nlm.nih.gov/38359852/ | not available | January 2024 |
38,048,298 | ASHP and ASHP Foundation Pharmacy Forecast 2024: Strategic Planning Guidance for Pharmacy Departments in Hospitals and Health Systems. | DiPiro JT; Hoffman JM; Schweitzer P; Chisholm-Burns MA; Nesbit TW; Fabian TJ; Cunningham FE; Barrett A; Fine MJ; Tichy E; Hernandez I; Scott CM; Norman C; Nelson SD; Kumah-Crystal Y | PURPOSE: The 2024 ASHP Pharmacy Forecast identifies and contextualizes emerging issues and trends that will influence healthcare, health systems, and the pharmacy profession and provides recommendations to inform long-term strategic planning that should prompt action by pharmacists and health-system leaders. METHODS: Drawing on the "wisdom of crowds" concept, a survey was constructed with 6 general themes, each with 6 to 9 focused statements and a seventh theme on preparedness (58 survey items in total). The size of and representation within the survey panel were intended to capture opinions from a wide range of pharmacy leaders. The survey instructed panelists to consider the likelihood of the events/scenarios described in the statements occurring in the next 5 years as being likely, somewhat likely, somewhat unlikely, or very unlikely. Then, survey panelists assessed the preparedness (from very unprepared to very prepared) for 12 of the statements. RESULTS: The 6 survey themes identified were Urgent Public Health Priorities, Responding to the Mental Health Crisis, Achieving Care Equity, New Disease Paradigms and Treatment Innovations, Workforce: Focus on Culture for the Future, and Artificial Intelligence: Can Ethics and Regulators Catch Up? The survey was completed by 250 respondents, yielding an 88% response rate. Analysis of survey results was provided by chapter authors along with strategic recommendations to guide actions for each theme. CONCLUSION: The focus of the Pharmacy Forecast is on large-scale, long-term trends that will influence healthcare and the pharmacy profession over months and years and not on day-to-day situational dynamics. The report provides insight to stimulate thinking and discussion and provides a starting point to proactively position leaders, their teams, and departments for potential future events and trends. | Humans; United States; Strategic Planning; *Pharmacy; *Pharmaceutical Services; *Pharmacies; Hospitals; *Pharmacy Service, Hospital; Societies, Pharmaceutical; Pharmacists; Surveys and Questionnaires | American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists | 2024 Jan 5 | 10.1093/ajhp/zxad231 [doi] | https://pubmed.ncbi.nlm.nih.gov/38048298/ | not available | January 2024 |
38,007,987 | Evaluating artificial intelligence software for delineating hemorrhage extent on CT brain imaging in stroke: AI delineation of ICH on CT. | Vacek A; Mair G; White P; Bath PM; Muir KW; Al-Shahi Salman R; Martin C; Dye D; Chappell FM; von Kummer R; Macleod M; Sprigg N; Wardlaw JM | BACKGROUND: The extent and distribution of intracranial hemorrhage (ICH) directly affects clinical management. Artificial intelligence (AI) software can detect and may delineate ICH extent on brain CT. We evaluated e-ASPECTS software (Brainomix Ltd.) performance for ICH delineation. METHODS: We qualitatively assessed software delineation of ICH on CT using patients from six stroke trials. We assessed hemorrhage delineation in five compartments: lobar, deep, posterior fossa, intraventricular, extra-axial. We categorized delineation as excellent, good, moderate, or poor. We assessed quality of software delineation with number of affected compartments in univariate analysis (Kruskall-Wallis test) and ICH location using logistic regression (dependent variable: dichotomous delineation categories 'excellent-good' versus 'moderate-poor'), and report odds ratios (OR) and 95 % confidence intervals (95 %CI). RESULTS: From 651 patients with ICH (median age 75 years, 53 % male), we included 628 with assessable CTs. Software delineation of ICH extent was 'excellent' in 189/628 (30 %), 'good' in 255/628 (41 %), 'moderate' in 127/628 (20 %), and 'poor' in 57/628 cases (9 %). The quality of software delineation of ICH was better when fewer compartments were affected (Z = 3.61-6.27; p = 0.0063). Software delineation of ICH extent was more likely to be 'excellent-good' quality when lobar alone (OR = 1.56, 95 %CI = 0.97-2.53) but 'moderate-poor' with any intraventricular (OR = 0.56, 95 %CI = 0.39-0.81, p = 0.002) or any extra-axial (OR = 0.41, 95 %CI = 0.27-0.62, p<0.001) extension. CONCLUSIONS: Delineation of ICH extent on stroke CT scans by AI software was excellent or good in 71 % of cases but was more likely to over- or under-estimate extent when ICH was either more extensive, intraventricular, or extra-axial. | Humans; Male; Aged; Female; *Cerebral Hemorrhage/diagnostic imaging; Artificial Intelligence; *Stroke/diagnostic imaging; Intracranial Hemorrhages/diagnostic imaging; Tomography, X-Ray Computed; Software; Neuroimaging | Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association | 2024 Jan | S1052-3057(23)00533-5 [pii] 10.1016/j.jstrokecerebrovasdis.2023.107512 [doi] | https://pubmed.ncbi.nlm.nih.gov/38007987/ | not available | January 2024 |
37,596,138 | Bone Metastasis in Prostate Cancer: Bone Scan Versus PET Imaging. | Mohseninia N; Zamani-Siahkali N; Harsini S; Divband G; Pirich C; Beheshti M | Prostate cancer is the second most common cause of malignancy among men, with bone metastasis being a significant source of morbidity and mortality in advanced cases. Detecting and treating bone metastasis at an early stage is crucial to improve the quality of life and survival of prostate cancer patients. This objective strongly relies on imaging studies. While CT and MRI have their specific utilities, they also possess certain drawbacks. Bone scintigraphy, although cost-effective and widely available, presents high false-positive rates. The emergence of PET/CT and PET/MRI, with their ability to overcome the limitations of standard imaging methods, offers promising alternatives for the detection of bone metastasis. Various radiotracers targeting cell division activity or cancer-specific membrane proteins, as well as bone seeking agents, have been developed and tested. The use of positron-emitting isotopes such as fluorine-18 and gallium-68 for labeling allows for a reduced radiation dose and unaffected biological properties. Furthermore, the integration of artificial intelligence (AI) and radiomics techniques in medical imaging has shown significant advancements in reducing interobserver variability, improving accuracy, and saving time. This article provides an overview of the advantages and limitations of bone scan using SPECT and SPECT/CT and PET imaging methods with different radiopharmaceuticals and highlights recent developments in hybrid scanners, AI, and radiomics for the identification of prostate cancer bone metastasis using molecular imaging. | Male; Humans; Positron Emission Tomography Computed Tomography/methods; Artificial Intelligence; Quality of Life; Positron-Emission Tomography/methods; *Bone Neoplasms/diagnostic imaging/secondary; Radiopharmaceuticals; *Prostatic Neoplasms/pathology; Gallium Radioisotopes | Seminars in nuclear medicine | 2024 Jan | S0001-2998(23)00057-0 [pii] 10.1053/j.semnuclmed.2023.07.004 [doi] | https://pubmed.ncbi.nlm.nih.gov/37596138/ | not available | January 2024 |
38,298,326 | Artificial Intelligence: Knowledge and Attitude Among Lebanese Medical Students. | Daher OA; Dabbousi AA; Chamroukh R; Saab AY; Al Ayoubi AR; Salameh P | Background Artificial intelligence (AI) has taken on a variety of functions in the medical field, and research has proven that it can address complicated issues in various applications. It is unknown whether Lebanese medical students and residents have a detailed understanding of this concept, and little is known about their attitudes toward AI. Aim This study fills a critical gap by revealing the knowledge and attitude of Lebanese medical students toward AI. Methods A multi-centric survey targeting 365 medical students from seven medical schools across Lebanon was conducted to assess their knowledge of and attitudes toward AI in medicine. The survey consists of five sections: the first part includes socio-demographic variables, while the second comprises the 'Medical Artificial Intelligence Readiness Scale' for medical students. The third part focuses on attitudes toward AI in medicine, the fourth assesses understanding of deep learning, and the fifth targets considerations of radiology as a specialization. Results There is a notable awareness of AI among students who are eager to learn about it. Despite this interest, there exists a gap in knowledge regarding deep learning, albeit alongside a positive attitude towards it. Students who are more open to embracing AI technology tend to have a better understanding of AI concepts (p=0.001). Additionally, a higher percentage of students from Mount Lebanon (71.6%) showed an inclination towards using AI compared to Beirut (63.2%) (p=0.03). Noteworthy are the Lebanese University and Saint Joseph University, where the highest proportions of students are willing to integrate AI into the medical field (79.4% and 76.7%, respectively; p=0.001). Conclusion It was concluded that most Lebanese medical students might not necessarily comprehend the core technological ideas of AI and deep learning. This lack of understanding was evident from the substantial amount of misinformation among the students. Consequently, there appears to be a significant demand for the inclusion of AI technologies in Lebanese medical school courses. | null | Cureus | 2024 Jan | 10.7759/cureus.51466 [doi] e51466 | https://pubmed.ncbi.nlm.nih.gov/38298326/ | not available | January 2024 |
37,929,807 | Preoperative detection of lymphovascular invasion in rectal cancer using intravoxel incoherent motion imaging based on radiomics. | Wong C; Liu T; Zhang C; Li M; Zhang H; Wang Q; Fu Y | BACKGROUND: Lymphovascular invasion (LVI) status plays an important role in treatment decision-making in rectal cancer (RC). Intravoxel incoherent motion (IVIM) imaging has been shown to detect LVI; however, making better use of IVIM data remains an important issue that needs to be discussed. PURPOSE: We proposed to explore the best way to use IVIM quantitative parameters and images to construct radiomics models for the noninvasive detection of LVI in RC. METHODS: A total of 83 patients (LVI negative (LVI-): LVI positive (LVI+) = 51:32) with postoperative pathology-confirmed LVI status in RC were divided into a training group (n = 58) and a validation group (n = 25). Images were acquired from a 3.0 Tesla machine, including oblique axial T2 weighted imaging (T2WI) and IVIM with 11 b values. The ADC, D, D(*) and f values were measured on IVIM maps. The ROIs of tumors were delineated on T2WI, DWI, ADC(map) , and D(map) images, and three mapping methods were used: ROIs_mapping from DWI, ROIs_mapping from ADC(map) , and ROIs_mapping from D(map) . Three-dimensional radiomics features were extracted from the delineated ROIs. Multivariate logistic regression was used for radiomics feature selection. Radiomics models based on different mapping methods were developed. Receiver operating characteristic (ROC) curves, calibration, and decision curve analyses (DCA) were used to evaluate the performance of the models. RESULTS: Model B, which was constructed with radiomics features from ADC(map) , D(map) and f(map) by "ROIs_mapping from DWI" and T2WI (AUC 0.894), performed better than other models based on single sequence (AUC 0.600-0.806) and even better than Model A, which was based on "ROIs_mapping from ADC" and T2WI (AUC 0.838). Furthermore, an integrated model was constructed with Model B and the IVIM parameter (f value) with an AUC of 0.920 (95% CI: 0.820-1.000), which was higher than that of Model B, in the validation group. CONCLUSIONS: The integrated model incorporating the radiomics features and IVIM parameters accurately detected LVI of RC. The "ROIs_mapping from DWI" method provided the best results. | Humans; *Radiomics; Magnetic Resonance Imaging/methods; *Rectal Neoplasms/diagnostic imaging/surgery/pathology; ROC Curve; Motion; Diffusion Magnetic Resonance Imaging/methods; Retrospective Studies | Medical physics | 2024 Jan | 10.1002/mp.16821 [doi] | https://pubmed.ncbi.nlm.nih.gov/37929807/ | not available | January 2024 |
37,944,140 | The Genie Is Out of the Bottle: What ChatGPT Can and Cannot Do for Medical Professionals. | Morales-Ramirez P; Mishek H; Dasgupta A | ChatGPT is a cutting-edge artificial intelligence technology that was released for public use in November 2022. Its rapid adoption has raised questions about capabilities, limitations, and risks. This article presents an overview of ChatGPT, and it highlights the current state of this technology for the medical field. The article seeks to provide a balanced perspective on what the model can and cannot do in three specific domains: clinical practice, research, and medical education. It also provides suggestions on how to optimize the use of this tool. | Humans; *Artificial Intelligence; Methylmethacrylates; *Education, Medical | Obstetrics and gynecology | 2024 Jan 1 | 10.1097/AOG.0000000000005446 [doi] | https://pubmed.ncbi.nlm.nih.gov/37944140/ | not available | January 2024 |
38,256,632 | A Review of Intraocular Lens Power Calculation Formulas Based on Artificial Intelligence. | Stopyra W; Cooke DL; Grzybowski A | PURPOSE: The proper selection of an intraocular lens power calculation formula is an essential aspect of cataract surgery. This study evaluated the accuracy of artificial intelligence-based formulas. DESIGN: Systematic review. METHODS: This review comprises articles evaluating the exactness of artificial intelligence-based formulas published from 2017 to July 2023. The papers were identified by a literature search of various databases (Pubmed/MEDLINE, Google Scholar, Crossref, Cochrane Library, Web of Science, and SciELO) using the terms "IOL formulas", "FullMonte", "Ladas", "Hill-RBF", "PEARL-DGS", "Kane", "Karmona", "Hoffer QST", and "Nallasamy". In total, 25 peer-reviewed articles in English with the maximum sample and the largest number of compared formulas were examined. RESULTS: The scores of the mean absolute error and percentage of patients within +/-0.5 D and +/-1.0 D were used to estimate the exactness of the formulas. In most studies the Kane formula obtained the smallest mean absolute error and the highest percentage of patients within +/-0.5 D and +/-1.0 D. Second place was typically achieved by the PEARL DGS formula. The limitations of the studies were also discussed. CONCLUSIONS: Kane seems to be the most accurate artificial intelligence-based formula. PEARL DGS also gives very good results. Hoffer QST, Karmona, and Nallasamy are the newest, and need further evaluation. | null | Journal of clinical medicine | 2024 Jan 16 | 10.3390/jcm13020498 [doi] 498 | https://pubmed.ncbi.nlm.nih.gov/38256632/ | not available | January 2024 |
37,985,056 | Functional magnetic alginate/gelatin sponge-based flexible sensor with multi-mode response and discrimination detection properties for human motion monitoring. | Fu Y; Wang S; Wan Z; Tian Y; Wang D; Ma Y; Yang L; Wei Z | The functional flexible sensors that can simultaneously detect multiple external excitations have exhibited great potential in the human-machine interaction and wearable electronics. However, it is still a primary challenge to develop a multi-mode sensor that can achieve sensitivity equilibrium towards different stimuli, and effectively recognize external stimulus while in a facile and cost-effective material and methodology. This study presented a functional flexible sensor based on natural polymer sodium alginate and gelatin sponge electrode which could detect both external mechanical and magnetic stimuli with superiorities of outstanding sensing capability and stability. With the optimal multilayered structure, it possessed high magnetic responsive sensitivity of 0.45 T(-1), excellent stability and recoverability. Its electrical property variations also displayed high sensitivity and durability under cyclic stretching, bending and compressing stimuli for 1000 cycles. More importantly, the sensor could not only respond to magnetic field and compression stimuli with contrary electrical responses, but also recognize the respective input signals to decouple different stimuli in real time. Furthermore, it was developed as electronic skins and smart sensor arrays for human physiological signals and mechanical-magnetic detection. Based on excellent multifunctional response characteristics, the sensor showed significant potential in next-generation intelligent multifunctional electronic system and artificial intelligence. | Humans; *Gelatin; Artificial Intelligence; Motion; *Wearable Electronic Devices; Magnetic Phenomena | Carbohydrate polymers | 2024 Jan 15 | S0144-8617(23)00985-2 [pii] 10.1016/j.carbpol.2023.121520 [doi] | https://pubmed.ncbi.nlm.nih.gov/37985056/ | not available | January 2024 |
37,855,067 | ChatGPT on guidelines: Providing contextual knowledge to GPT allows it to provide advice on appropriate colonoscopy intervals. | Lim DYZ; Tan YB; Koh JTE; Tung JYM; Sng GGR; Tan DMY; Tan CK | BACKGROUND AND AIM: Colonoscopy is commonly used in screening and surveillance for colorectal cancer. Multiple different guidelines provide recommendations on the interval between colonoscopies. This can be challenging for non-specialist healthcare providers to navigate. Large language models like ChatGPT are a potential tool for parsing patient histories and providing advice. However, the standard GPT model is not designed for medical use and can hallucinate. One way to overcome these challenges is to provide contextual information with medical guidelines to help the model respond accurately to queries. Our study compares the standard GPT4 against a contextualized model provided with relevant screening guidelines. We evaluated whether the models could provide correct advice for screening and surveillance intervals for colonoscopy. METHODS: Relevant guidelines pertaining to colorectal cancer screening and surveillance were formulated into a knowledge base for GPT. We tested 62 example case scenarios (three times each) on standard GPT4 and on a contextualized model with the knowledge base. RESULTS: The contextualized GPT4 model outperformed the standard GPT4 in all domains. No high-risk features were missed, and only two cases had hallucination of additional high-risk features. A correct interval to colonoscopy was provided in the majority of cases. Guidelines were appropriately cited in almost all cases. CONCLUSIONS: A contextualized GPT4 model could identify high-risk features and quote appropriate guidelines without significant hallucination. It gave a correct interval to the next colonoscopy in the majority of cases. This provides proof of concept that ChatGPT with appropriate refinement can serve as an accurate physician assistant. | Humans; *Colonoscopy; *Colorectal Neoplasms/diagnosis/prevention & control/epidemiology; Risk Factors; Early Detection of Cancer; Hallucinations | Journal of gastroenterology and hepatology | 2024 Jan | 10.1111/jgh.16375 [doi] | https://pubmed.ncbi.nlm.nih.gov/37855067/ | not available | January 2024 |
40,060,265 | Emergency Medicine Assistants in the Field of Toxicology, Comparison of ChatGPT-3.5 and GEMINI Artificial Intelligence Systems. | Bedel HA; Bedel C; Selvi F; Zortuk O; Karanci Y | OBJECTIVE: Artificial intelligence models human thinking and problem-solving abilities, allowing computers to make autonomous decisions. There is a lack of studies demonstrating the clinical utility of GPT and Gemin in the field of toxicology, which means their level of competence is not well understood. This study compares the responses given by GPT-3.5 and Gemin to those provided by emergency medicine residents. METHODS: This prospective study was focused on toxicology and utilized the widely recognized educational resource 'Tintinalli Emergency Medicine: A Comprehensive Study Guide' for the field of Emergency Medicine. A set of twenty questions, each with five options, was devised to test knowledge of toxicological data as defined in the book. These questions were then used to train ChatGPT GPT-3.5 (Generative Pre-trained Transformer 3.5) by OpenAI and Gemini by Google AI in the clinic. The resulting answers were then meticulously analyzed. RESULTS: 28 physicians, 35.7% of whom were women, were included in our study. A comparison was made between the physician and AI scores. While a significant difference was found in the comparison (F=2.368 and p<0.001), no significant difference was found between the two groups in the post-hoc Tukey test. GPT-3.5 mean score is 9.9+/-0.71, Gemini mean score is 11.30+/-1.17 and, physicians' mean score is 9.82+/-3.70 (Figure 1). CONCLUSIONS: It is clear that GPT-3.5 and Gemini respond similarly to topics in toxicology, just as resident physicians do. | null | Acta medica Lituanica | 2024 | 10.15388/Amed.2024.31.2.18 [doi] | https://pubmed.ncbi.nlm.nih.gov/40060265/ | not available | January 2024 |
38,227,357 | Enabling Personalization for Digital Cognitive Stimulation to Support Communication With People With Dementia: Pilot Intervention Study as a Prelude to AI Development. | Hird N; Osaki T; Ghosh S; Palaniappan SK; Maeda K | BACKGROUND: Maintaining good communication and engagement between people with dementia and their caregivers is a major challenge in dementia care. Cognitive stimulation is a psychosocial intervention that supports communication and engagement, and several digital applications for cognitive stimulation have been developed. Personalization is an important factor for obtaining sustainable benefits, but the time and effort required to personalize and optimize applications often makes them difficult for routine use by nonspecialist caregivers and families. Although artificial intelligence (AI) has great potential to support automation of the personalization process, its use is largely unexplored because of the lack of suitable data from which to develop and train machine learning models. OBJECTIVE: This pilot study aims to evaluate a digital application called Aikomi in Japanese care homes for its potential to (1) create and deliver personalized cognitive stimulation programs to promote communication and engagement between people with dementia and usual care staff and (2) capture meaningful personalized data suitable for the development of AI systems. METHODS: A modular technology platform was developed and used to create personalized programs for 15 people with dementia living in 4 residential care facilities in Japan with the cooperation of a family member or care staff. A single intervention with the program was conducted with the person with dementia together with a care staff member, and for some participants, smell stimulation was provided using selected smell sticks in conjunction with the digital program. All sessions were recorded using a video camera, and the combined personalized data obtained by the platform were analyzed. RESULTS: Most people with dementia (10/15, 67%) showed high levels of engagement (>40 on Engagement of a Person with Dementia Scale), and there were no incidences of negative reactions toward the programs. Care staff reported that some participants showed extended concentration and spontaneous communication while using Aikomi, which was not their usual behavior. Smell stimulation promoted engagement for some participants even when they were unable to identify the smell. No changes in well-being were observed following the intervention according to the Mental Function Impairment Scale. The level of response to each type of content in the stimulation program varied greatly according to the person with dementia, and personalized data captured by the Aikomi platform enabled understanding of correlations between stimulation content and responses for each participant. CONCLUSIONS: This study suggests that the Aikomi digital application is acceptable for use by persons with dementia and care staff and may have the potential to promote communication and engagement. The platform captures personalized data, which can provide suitable input for machine learning. Further investigation of Aikomi will be conducted to develop AI systems and create personalized digital cognitive stimulation applications that can be easily used by nonspecialist caregivers. | null | JMIR formative research | 2024 Jan 16 | 10.2196/51732 [doi] e51732 | https://pubmed.ncbi.nlm.nih.gov/38227357/ | not available | January 2024 |
38,274,051 | Construction and validation of a method for automated time label segmentation of heart sounds. | Li L; Huang M; Dao L; Feng X; Liu Y; Wei C; Liu F; Zhang J; Xu F | Heart sound detection technology plays an important role in the prediction of cardiovascular disease, but the most significant heart sounds are fleeting and may be imperceptible. Hence, obtaining heart sound information in an efficient and accurate manner will be helpful for the prediction and diagnosis of heart disease. To obtain heart sound information, we designed an audio data analysis tool to segment the heart sounds from single heart cycle, and validated the heart rate using a finger oxygen meter. The results from our validated technique could be used to realize heart sound segmentation. Our robust algorithmic platform was able to segment the heart sounds, which could then be compared in terms of their difference from the background. A combination of an electronic stethoscope and artificial intelligence technology was used for the digital collection of heart sounds and the intelligent identification of the first (S1) and second (S2) heart sounds. Our approach can provide an objective basis for the auscultation of heart sounds and visual display of heart sounds and murmurs. | null | Frontiers in artificial intelligence | 2023 | 10.3389/frai.2023.1309750 [doi] 1309750 | https://pubmed.ncbi.nlm.nih.gov/38274051/ | not available | January 2024 |
38,006,825 | Unraveling the motion and deformation characteristics of red blood cells in a deterministic lateral displacement device. | Liu S; Chen S; Xiao L; Zhang K; Qi Y; Li H; Cheng Y; Hu Z; Lin C | Deterministic Lateral Displacement (DLD) device has gained widespread recognition and trusted for filtering blood cells. However, there remains a crucial need to explore the complex interplay between deformable cells and flow within the DLD device to improve its design. This paper presents an approach utilizing a mesoscopic cell-level numerical model based on dissipative particle dynamics to effectively capture this complex phenomenon. To establish the model's credibility, a series of numerical simulations were conducted and the numerical results were validated with nominal experimental data from the literature. These include single cell stretching experiment, comparisons of the morphological characteristics of cells in DLD, and comparison the specific row-shift fraction of DLD required to initiate the zigzag mode. Additionally, we investigate the effect of cell rigidity, which serves as an indicator of cell health, on average flow velocity, trajectory, and asphericity. Moreover, we extend the existing theory of predicting zigzag mode for solid spherical particles to encompass the behavior of red blood cells. To achieve this, we introduce a new concept of effective diameter and demonstrate its applicability in providing highly accurate predictions across a wide range of conditions. | *Erythrocytes; *Erythrocyte Deformability; Filtration | Computers in biology and medicine | 2024 Jan | S0010-4825(23)01177-0 [pii] 10.1016/j.compbiomed.2023.107712 [doi] | https://pubmed.ncbi.nlm.nih.gov/38006825/ | not available | January 2024 |
38,179,234 | Comment on the use of artificial intelligence in writing scientific papers. | Daungsupawong H; Wiwanitkit V | null | null | Brain communications | 2024 | 10.1093/braincomms/fcad354 [doi] fcad354 | https://pubmed.ncbi.nlm.nih.gov/38179234/ | not available | January 2024 |
38,050,138 | Grand rounds in methodology: key considerations for implementing machine learning solutions in quality improvement initiatives. | Verma AA; Trbovich P; Mamdani M; Shojania KG | Machine learning (ML) solutions are increasingly entering healthcare. They are complex, sociotechnical systems that include data inputs, ML models, technical infrastructure and human interactions. They have promise for improving care across a wide range of clinical applications but if poorly implemented, they may disrupt clinical workflows, exacerbate inequities in care and harm patients. Many aspects of ML solutions are similar to other digital technologies, which have well-established approaches to implementation. However, ML applications present distinct implementation challenges, given that their predictions are often complex and difficult to understand, they can be influenced by biases in the data sets used to develop them, and their impacts on human behaviour are poorly understood. This manuscript summarises the current state of knowledge about implementing ML solutions in clinical care and offers practical guidance for implementation. We propose three overarching questions for potential users to consider when deploying ML solutions in clinical care: (1) Is a clinical or operational problem likely to be addressed by an ML solution? (2) How can an ML solution be evaluated to determine its readiness for deployment? (3) How can an ML solution be deployed and maintained optimally? The Quality Improvement community has an essential role to play in ensuring that ML solutions are translated into clinical practice safely, effectively, and ethically. | Humans; *Quality Improvement; *Teaching Rounds; Delivery of Health Care; Machine Learning | BMJ quality & safety | 2024 Jan 19 | 10.1136/bmjqs-2022-015713 [doi] | https://pubmed.ncbi.nlm.nih.gov/38050138/ | not available | January 2024 |
37,864,888 | Before-after safety evaluation of part-time protected right-turn signals: An extreme value theory approach by applying artificial intelligence-based video analytics. | Howlader MM; Ali Y; Burbridge A; Haque MM | Extreme value theory models have opened doors for before-after safety evaluation of engineering treatments using traffic conflict techniques. Recent advancements in automated conflict extraction technologies have further expedited conflict-based safety evaluation as a potential alternative to traditional crash-based methods. However, the suitability of extreme value theory models in the before-after evaluation of engineering treatments needs to be rigorously tested. As such, this study proposes a traffic conflict-based before-after evaluation of a novel part-time protected right-turn signal strategy for right-turn or opposing-through crashes at signalised intersections. A part-time protected right-turn signal strategy refers to a signal arrangement where permissive and fully protected right-turn phasings are operated during peak and off-peak hours, respectively. A deep neural network-based computer vision technique was applied to extract the conflicts from a total of 654 h of video recordings (before period: 266 h and after period: 388 h) over seven treated approaches, and four matching control approaches at five signalised intersections in the city of Cairns, Australia. Using post encroachment time and post-collision velocity difference as traffic conflict measures, non-stationary bivariate generalised extreme value models were developed to estimate the severe and non-severe opposing-through crashes at signal cycle levels. The odds ratio analysis of model-predicted crash risks suggests that part-time protected right-turn signals reduce 67% and 81% of severe and non-severe opposing-through crashes at signalised intersections, respectively. Part-time protected right-turn signal strategy offers a good safety solution without precipitating need for capacity upgrades to accommodate queued right turners at signalised intersections. | Humans; *Accidents, Traffic/prevention & control; *Artificial Intelligence; Environment Design; Cities; Neural Networks, Computer; Safety | Accident; analysis and prevention | 2024 Jan | S0001-4575(23)00388-3 [pii] 10.1016/j.aap.2023.107341 [doi] | https://pubmed.ncbi.nlm.nih.gov/37864888/ | not available | January 2024 |
38,272,802 | Prediction of amputation risk of patients with diabetic foot using classification algorithms: A clinical study from a tertiary center. | Demirkol D; Erol CS; Tannier X; Ozcan T; Aktas S | Diabetic foot ulcers can have vital consequences, such as amputation for patients. The primary purpose of this study is to predict the amputation risk of diabetic foot patients using machine-learning classification algorithms. In this research, 407 patients treated with the diagnosis of diabetic foot between January 2009-September 2019 in Istanbul University Faculty of Medicine in the Department of Undersea and Hyperbaric Medicine were retrospectively evaluated. Principal Component Analysis (PCA) was used to identify the key features associated with the amputation risk in diabetic foot patients within the dataset. Thus, various prediction/classification models were created to predict the "overall" risk of diabetic foot patients. Predictive machine-learning models were created using various algorithms. Additionally to optimize the hyperparameters of the Random Forest Algorithm (RF), experimental use of Bayesian Optimization (BO) has been employed. The sub-dimension data set comprising categorical and numerical values was subjected to a feature selection procedure. Among all the algorithms tested under the defined experimental conditions, the BO-optimized "RF" based on the hybrid approach (PCA-RF-BO) and "Logistic Regression" algorithms demonstrated superior performance with 85% and 90% test accuracies, respectively. In conclusion, our findings would serve as an essential benchmark, offering valuable guidance in reducing such hazards. | Humans; *Diabetic Foot/surgery/diagnosis; Retrospective Studies; Bayes Theorem; Algorithms; Amputation, Surgical; *Diabetes Mellitus | International wound journal | 2024 Jan | 10.1111/iwj.14556 [doi] e14556 | https://pubmed.ncbi.nlm.nih.gov/38272802/ | not available | January 2024 |
37,811,866 | NIH Fifth Artificial Pancreas Workshop 2023: Meeting Report: The Fifth Artificial Pancreas Workshop: Enabling Fully Automation, Access, and Adoption. | Aaron RE; Tian T; Yeung AM; Huang J; Arreaza-Rubin GA; Ginsberg BH; Kompala T; Lee WA; Kerr D; Colmegna P; Mendez CE; Muchmore DB; Wallia A; Klonoff DC | The Fifth Artificial Pancreas Workshop: Enabling Fully Automation, Access, and Adoption was held at the National Institutes of Health (NIH) Campus in Bethesda, Maryland on May 1 to 2, 2023. The organizing Committee included representatives of NIH, the US Food and Drug Administration (FDA), Diabetes Technology Society, Juvenile Diabetes Research Foundation (JDRF), and the Leona M. and Harry B. Helmsley Charitable Trust. In previous years, the NIH Division of Diabetes, Endocrinology, and Metabolic Diseases along with other diabetes organizations had organized periodic workshops, and it had been seven years since the NIH hosted the Fourth Artificial Pancreas in July 2016. Since then, significant improvements in insulin delivery have occurred. Several automated insulin delivery (AID) systems are now commercially available. The workshop featured sessions on: (1) Lessons Learned from Recent Advanced Clinical Trials and Real-World Data Analysis, (2) Interoperability, Data Management, Integration of Systems, and Cybersecurity, Challenges and Regulatory Considerations, (3) Adaptation of Systems Through the Lifespan and Special Populations: Are Specific Algorithms Needed, (4) Development of Adaptive Algorithms for Insulin Only and for Multihormonal Systems or Combination with Adjuvant Therapies and Drugs: Clinical Expected Outcomes and Public Health Impact, (5) Novel Artificial Intelligence Strategies to Develop Smarter, More Automated, Personalized Diabetes Management Systems, (6) Novel Sensing Strategies, Hormone Formulations and Delivery to Optimize Close-loop Systems, (7) Special Topic: Clinical and Real-world Viability of IP-IP Systems. "Fully automated closed-loop insulin delivery using the IP route," (8) Round-table Panel: Closed-loop performance: What to Expect and What are the Best Metrics to Assess it, and (9) Round-table Discussion: What is Needed for More Adaptable, Accessible, and Usable Future Generation of Systems? How to Promote Equitable Innovation? This article summarizes the discussions of the Workshop. | Humans; *Diabetes Mellitus, Type 1/drug therapy; Insulin/therapeutic use; *Pancreas, Artificial; Blood Glucose; Artificial Intelligence; Insulin Infusion Systems; Insulin, Regular, Human/therapeutic use; Automation; Hypoglycemic Agents/therapeutic use | Journal of diabetes science and technology | 2024 Jan | 10.1177/19322968231201829 [doi] | https://pubmed.ncbi.nlm.nih.gov/37811866/ | not available | January 2024 |
39,235,485 | Innovation at the Interface between Academia and Industry: The BioMed X Model. | Cristian FB; Tidona C; Ruckle T | In the evolving landscape of biomedical research, the convergence of molecular biology and translational medicine has ushered in a new era of pharmaceutical innovation. This paradigm shift, characterized by significant advances in targeted therapies and gene editing, emphasizes the critical role of integrating academic research - and academic researchers - within industry settings. Contemporary innovation models are moving beyond traditional, corporation-centered frameworks, adopting more open, collaborative approaches. Here, we discuss the challenges and solutions brought about by this new direction in pharma innovation and describe the BioMed X innovation model, a unique open innovation approach that has been growing continuously over the past ten years. | Humans; *Drug Industry; Translational Research, Biomedical; Biomedical Research; Diffusion of Innovation; Academia | Handbook of experimental pharmacology | 2024 | 10.1007/164_2024_729 [doi] | https://pubmed.ncbi.nlm.nih.gov/39235485/ | not available | January 2024 |
38,304,436 | How to reload and upgrade digital health to serve the healthcare needs of Nigerians. | Ravi N; Thomas C; Odogwu J | null | null | Frontiers in digital health | 2023 | 10.3389/fdgth.2023.1225092 [doi] 1225092 | https://pubmed.ncbi.nlm.nih.gov/38304436/ | not available | January 2024 |
39,010,830 | U.S. payer budget impact of using an AI-augmented cancer risk discrimination digital histopathology platform to identify high-risk of recurrence in women with early-stage invasive breast cancer. | Masud SF; Mark N; Goss T; Malinowski D; Schnitt SJ; Sparano JA; Donovan MJ | AIMS: Use of gene expression signatures to predict adjuvant chemotherapy benefit in women with early-stage breast cancer is increasing. However, high cost, limited access, and eligibility for these tests results in the adoption of less precise assessment approaches. This study evaluates the cost impact of PreciseDx Breast (PDxBr), an AI-augmented histopathology platform that assesses the 6-year risk of recurrence in early-stage invasive breast cancer patients to help improve informed use of adjuvant chemotherapy. MATERIALS AND METHODS: A decision-tree Markov model was developed to compare the costs of treatment guided by standard of care (SOC) risk assessment (i.e. clinical diagnostic workup with or without Oncotype DX) versus PDxBr with SOC in a hypothetical cohort of U.S. women with early-stage invasive breast cancer. A commercial payer perspective compares costs of testing, adjuvant therapy, recurrence, adverse events, surveillance, and end-of-life care. RESULTS: PDxBr use in prognostic evaluation resulted in savings of $4 million (M) in year one compared to current SOC in 1 M females members. Over 6-years, savings increased to $12.5 M. The per-treated patient costs in year one amounted to $19.5 thousand (K) for SOC and $16.9K for PDxBr. LIMITATIONS: For simplicity, recurrence was not specified. We performed scenario analyses to account for variations in rates for local, regional, and distant recurrence. Second, a recurrent patient incurs the total cost of treated recurrence in the first year and goes back to remission or death. Third, CDK4/6i treatment is only incorporated in the recurrence costs but not in the first line of treatment for early-stage breast cancer due to limited data. CONCLUSIONS: Sensitivity analyses demonstrated robust overall savings to changes in all variables in the model. The use of PDxBr to assess breast cancer recurrence risk has the potential to fill gaps in care and reduce costs when gene expression signatures are not available. | Humans; *Breast Neoplasms/pathology; Female; *Neoplasm Recurrence, Local; *Markov Chains; Risk Assessment; Decision Trees; Chemotherapy, Adjuvant/economics; Cost-Benefit Analysis; United States; Artificial Intelligence; Neoplasm Staging; Middle Aged | Journal of medical economics | 2024 Jan-Dec | 10.1080/13696998.2024.2379211 [doi] | https://pubmed.ncbi.nlm.nih.gov/39010830/ | not available | January 2024 |
38,825,779 | Untapped potential of gut microbiome for hypertension management. | Gao K; Wang PX; Mei X; Yang T; Yu K | The gut microbiota has been shown to be associated with a range of illnesses and disorders, including hypertension, which is recognized as the primary factor contributing to the development of serious cardiovascular diseases. In this review, we conducted a comprehensive analysis of the progression of the research domain pertaining to gut microbiota and hypertension. Our primary emphasis was on the interplay between gut microbiota and blood pressure that are mediated by host and gut microbiota-derived metabolites. Additionally, we elaborate the reciprocal communication between gut microbiota and antihypertensive drugs, and its influence on the blood pressure of the host. The field of computer science has seen rapid progress with its great potential in the application in biomedical sciences, we prompt an exploration of the use of microbiome databases and artificial intelligence in the realm of high blood pressure prediction and prevention. We propose the use of gut microbiota as potential biomarkers in the context of hypertension prevention and therapy. | *Gastrointestinal Microbiome/physiology; Humans; *Hypertension/microbiology; *Antihypertensive Agents/therapeutic use; *Blood Pressure; Animals; Bacteria/classification/metabolism/genetics/isolation & purification | Gut microbes | 2024 Jan-Dec | 10.1080/19490976.2024.2356278 [doi] 2356278 | https://pubmed.ncbi.nlm.nih.gov/38825779/ | not available | January 2024 |
38,279,081 | ECDEP: identifying essential proteins based on evolutionary community discovery and subcellular localization. | Ye C; Wu Q; Chen S; Zhang X; Xu W; Wu Y; Zhang Y; Yue Y | BACKGROUND: In cellular activities, essential proteins play a vital role and are instrumental in comprehending fundamental biological necessities and identifying pathogenic genes. Current deep learning approaches for predicting essential proteins underutilize the potential of gene expression data and are inadequate for the exploration of dynamic networks with limited evaluation across diverse species. RESULTS: We introduce ECDEP, an essential protein identification model based on evolutionary community discovery. ECDEP integrates temporal gene expression data with a protein-protein interaction (PPI) network and employs the 3-Sigma rule to eliminate outliers at each time point, constructing a dynamic network. Next, we utilize edge birth and death information to establish an interaction streaming source to feed into the evolutionary community discovery algorithm and then identify overlapping communities during the evolution of the dynamic network. SVM recursive feature elimination (RFE) is applied to extract the most informative communities, which are combined with subcellular localization data for classification predictions. We assess the performance of ECDEP by comparing it against ten centrality methods, four shallow machine learning methods with RFE, and two deep learning methods that incorporate multiple biological data sources on Saccharomyces. Cerevisiae (S. cerevisiae), Homo sapiens (H. sapiens), Mus musculus, and Caenorhabditis elegans. ECDEP achieves an AP value of 0.86 on the H. sapiens dataset and the contribution ratio of community features in classification reaches 0.54 on the S. cerevisiae (Krogan) dataset. CONCLUSIONS: Our proposed method adeptly integrates network dynamics and yields outstanding results across various datasets. Furthermore, the incorporation of evolutionary community discovery algorithms amplifies the capacity of gene expression data in classification. | Animals; Mice; Humans; *Saccharomyces cerevisiae/genetics/metabolism; *Protein Interaction Maps; Algorithms; Proteins/metabolism; Caenorhabditis elegans/genetics/metabolism | BMC genomics | 2024 Jan 26 | 10.1186/s12864-024-10019-5 [doi] 117 | https://pubmed.ncbi.nlm.nih.gov/38279081/ | not available | January 2024 |
38,217,733 | Artificial intelligence for classification and detection of oral mucosa lesions on photographs: a systematic review and meta-analysis. | Rokhshad R; Mohammad-Rahimi H; Price JB; Shoorgashti R; Abbasiparashkouh Z; Esmaeili M; Sarfaraz B; Rokhshad A; Motamedian SR; Soltani P; Schwendicke F | OBJECTIVE: This study aimed to review and synthesize studies using artificial intelligence (AI) for classifying, detecting, or segmenting oral mucosal lesions on photographs. MATERIALS AND METHOD: Inclusion criteria were (1) studies employing AI to (2) classify, detect, or segment oral mucosa lesions, (3) on oral photographs of human subjects. Included studies were assessed for risk of bias using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). A PubMed, Scopus, Embase, Web of Science, IEEE, arXiv, medRxiv, and grey literature (Google Scholar) search was conducted until June 2023, without language limitation. RESULTS: After initial searching, 36 eligible studies (from 8734 identified records) were included. Based on QUADAS-2, only 7% of studies were at low risk of bias for all domains. Studies employed different AI models and reported a wide range of outcomes and metrics. The accuracy of AI for detecting oral mucosal lesions ranged from 74 to 100%, while that for clinicians un-aided by AI ranged from 61 to 98%. Pooled diagnostic odds ratio for studies which evaluated AI for diagnosing or discriminating potentially malignant lesions was 155 (95% confidence interval 23-1019), while that for cancerous lesions was 114 (59-221). CONCLUSIONS: AI may assist in oral mucosa lesion screening while the expected accuracy gains or further health benefits remain unclear so far. CLINICAL RELEVANCE: Artificial intelligence assists oral mucosa lesion screening and may foster more targeted testing and referral in the hands of non-specialist providers, for example. So far, it remains unclear if accuracy gains compared with specialized can be realized. | Humans; *Artificial Intelligence; *Mouth Mucosa; Referral and Consultation | Clinical oral investigations | 2024 Jan 13 | 10.1007/s00784-023-05475-4 [doi] | https://pubmed.ncbi.nlm.nih.gov/38217733/ | not available | January 2024 |
38,166,330 | The Scottish Medical Imaging Archive: 57.3 Million Radiology Studies Linked to Their Medical Records. | Baxter R; Nind T; Sutherland J; McAllister G; Hardy D; Hume A; MacLeod R; Caldwell J; Krueger S; Tramma L; Teviotdale R; Gillen K; Scobbie D; Baillie I; Brooks A; Prodan B; Kerr W; Sloan-Murphy D; Herrera JFR; van Beek EJR; Reel PS; Reel S; Mansouri-Benssassi E; Mudie R; Steele D; Doney A; Trucco E; Morris C; Wallace R; Morris A; Parsons M; Jefferson E | Keywords: MRI, Imaging Sequences, Ultrasound, Mammography, CT, Angiography, Conventional Radiography Published under a CC BY 4.0 license. See also the commentary by Whitman and Vining in this issue. | Radiography; *Mammography; Medical Records; *Radiology; Scotland | Radiology. Artificial intelligence | 2024 Jan | 10.1148/ryai.220266 [doi] e220266 | https://pubmed.ncbi.nlm.nih.gov/38166330/ | not available | January 2024 |
38,585,422 | An extension of the best-worst method based on the spherical fuzzy sets for multi-criteria decision-making. | Haseli G; Sheikh R; Ghoushchi SJ; Hajiaghaei-Keshteli M; Moslem S; Deveci M; Kadry S | The ambiguous information in multi-criteria decision-making (MCDM) and the vagueness of decision-makers for qualitative judgments necessitate accurate tools to overcome uncertainties and generate reliable solutions. As one of the latest and most powerful MCDM methods for obtaining criteria weight, the best-worst method (BWM) has been developed. Compared to other MCDM methods, such as the analytic hierarchy process, the BWM requires fewer pairwise comparisons and produces more consistent results. Consequently, the main objective of this study is to develop an extension of BWM using spherical fuzzy sets (SFS) to address MCDM problems under uncertain conditions. Hesitancy, non-membership, and membership degrees are three-dimensional functions included in the SFS. The presence of three defined degrees allows decision-makers to express their judgments more accurately. An optimization model based on nonlinear constraints is used to determine optimal spherical fuzzy weight coefficients (SF-BWM). Additionally, a consistency ratio is proposed for the SF-BWM to assess the reliability of the proposed method in comparison to other versions of BWM. SF-BWM is examined using two numerical decision-making problems. The results show that the proposed method based on the SF-BWM provided the criteria weights with the same priority as the BWM and fuzzy BWM. However, there are differences in the criteria weight values based on the SF-BWM that indicate the accuracy and reliability of the obtained results. The main advantage of using SF-BWM is providing a better consistency ratio. Based on the comparative analysis, the consistency ratio obtained for SF-BWM is threefold better than the BWM and fuzzy BWM methods, which leads to more accurate results than BWM and fuzzy BWM. | null | Granular computing | 2024 | 10.1007/s41066-024-00462-w [doi] 40 | https://pubmed.ncbi.nlm.nih.gov/38585422/ | not available | January 2024 |
38,269,845 | Making Digital Health Equitable. | Hammond WE; West VL | Most agree that the current healthcare system is broken. Fortunately, technology is increasing at an exponential rate and provides a solution for the future. Digital Health is an integrator concept that has the potential to take advantage of technological advantages. Digital Health converges health, healthcare, research, and everyday life. It includes technologies, platforms, and systems that engage consumers in all aspects of life. It makes health and healthcare be people-centered and personalized. Digital health requires total interoperability - standards, common data elements, and the integration of data from all sources. It demands data sharing. Digital Health brings together a wide range of stakeholders for similar goals using the same resources. Digital Health uses mobile devices and wearable sensors and uses Artificial Intelligence and Machine Learning to handle the vast amount of data Digital Health engages. Finally, Digital Health has the potential to open the gap between the different social and economic classes that must be addressed. | Humans; *Digital Health; *Artificial Intelligence; Common Data Elements; Computers, Handheld; Health Facilities | Studies in health technology and informatics | 2024 Jan 25 | 10.3233/SHTI231007 [doi] | https://pubmed.ncbi.nlm.nih.gov/38269845/ | not available | January 2024 |
38,940,257 | Current understanding of Duchenne muscular dystrophy - a purported interview with a purported expert. | Meyer-Szary J; Mikulski S | null | Humans; *Muscular Dystrophy, Duchenne/therapy/diagnosis | Cardiology journal | 2024 | 10.5603/cj.94330 [doi] | https://pubmed.ncbi.nlm.nih.gov/38940257/ | not available | January 2024 |
39,523,285 | Comprehensive Overview of Computational Modeling and Artificial Intelligence in Pediatric Neurosurgery. | Qiu S; Malhotra AK; Quon JL | In this chapter, we give an overview of artificial intelligence tools and their use thus far in pediatric neurosurgery. We discuss different machine learning algorithms from a data-driven approach in order to guide clinicians and scientists as they apply them to real-world datasets. We provide examples of their successful application as well as evaluate limitations and pitfalls specific to clinical use. Finally, we explore future directions and exciting new opportunities to take advantage of these tools as they continue to advance and evolve. | Humans; *Artificial Intelligence; Child; *Machine Learning; Neurosurgical Procedures/methods; Computer Simulation; Pediatrics/methods; Neurosurgery/methods; Algorithms | Advances in experimental medicine and biology | 2024 | 10.1007/978-3-031-64892-2_30 [doi] | https://pubmed.ncbi.nlm.nih.gov/39523285/ | not available | January 2024 |
39,271,261 | Applications of spatial transcriptomics and artificial intelligence to develop integrated management of pancreatic cancer. | Maurya R; Chug I; Vudatha V; Palma AM | Cancer is a complex disease intrinsically associated with cellular processes and gene expression. With the development of techniques such as single-cell sequencing and sequential fluorescence in situ hybridization (seqFISH), it was possible to map the location of cells based on their gene expression with more precision. Moreover, in recent years, many tools have been developed to analyze these extensive datasets by integrating machine learning and artificial intelligence in a comprehensive manner. Since these tools analyze sequencing data, they offer the chance to analyze any tissue regardless of its origin. By applying this to cancer settings, spatial transcriptomic analysis based on artificial intelligence may help us understand cell-cell communications within the tumor microenvironment. Another advantage of this analysis is the identification of new biomarkers and therapeutic targets. The integration of such analysis with other omics data and with routine exams such as magnetic resonance imaging can help physicians with the earlier diagnosis of tumors as well as establish a more personalized treatment for pancreatic cancer patients. In this review, we give an overview description of pancreatic cancer, describe how spatial transcriptomics and artificial intelligence have been used to study pancreatic cancer and provide examples of how integrating these tools may help physicians manage pancreatic cancer in a more personalized approach. | Humans; *Pancreatic Neoplasms/genetics/pathology/therapy; *Artificial Intelligence; *Transcriptome/genetics; Gene Expression Profiling/methods; Biomarkers, Tumor/genetics; Tumor Microenvironment/genetics; Disease Management; Machine Learning | Advances in cancer research | 2024 | S0065-230X(24)00025-3 [pii] 10.1016/bs.acr.2024.06.007 [doi] | https://pubmed.ncbi.nlm.nih.gov/39271261/ | not available | January 2024 |
38,284,601 | Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools. | Mu W; Kleter GA; Bouzembrak Y; Dupouy E; Frewer LJ; Radwan Al Natour FN; Marvin HJP | To enhance the resilience of food systems to food safety risks, it is vitally important for national authorities and international organizations to be able to identify emerging food safety risks and to provide early warning signals in a timely manner. This review provides an overview of existing and experimental applications of artificial intelligence (AI), big data, and internet of things as part of early warning and emerging risk identification tools and methods in the food safety domain. There is an ongoing rapid development of systems fed by numerous, real-time, and diverse data with the aim of early warning and identification of emerging food safety risks. The suitability of big data and AI to support such systems is illustrated by two cases in which climate change drives the emergence of risks, namely, harmful algal blooms affecting seafood and fungal growth and mycotoxin formation in crops. Automation and machine learning are crucial for the development of future real-time food safety risk early warning systems. Although these developments increase the feasibility and effectiveness of prospective early warning and emerging risk identification tools, their implementation may prove challenging, particularly for low- and middle-income countries due to low connectivity and data availability. It is advocated to overcome these challenges by improving the capability and capacity of national authorities, as well as by enhancing their collaboration with the private sector and international organizations. | Artificial Intelligence; Big Data; *Internet of Things; Prospective Studies; *Resilience, Psychological; Food Safety | Comprehensive reviews in food science and food safety | 2024 Jan | 10.1111/1541-4337.13296 [doi] | https://pubmed.ncbi.nlm.nih.gov/38284601/ | not available | January 2024 |
38,175,842 | Harnessing fluorescent carbon quantum dots from natural resource for advancing sweat latent fingerprint recognition with machine learning algorithms for enhanced human identification. | Yadav N; Mudgal D; Mishra A; Shukla S; Malik T; Mishra V | Nowadays, it is fascinating to engineer waste biomass into functional valuable nanomaterials. We investigate the production of hetero-atom doped carbon quantum dots (N-S@MCDs) to address the adaptability constraint in green precursors concerning the contents of the green precursors i.e., Tagetes erecta (marigold extract). The successful formation of N-S@MCDs as described has been validated by distinct analytical characterizations. As synthesized N-S@MCDs successfully incorporated on corn-starch powder, providing a nano-carbogenic fingerprint powder composition (N-S@MCDs/corn-starch phosphors). N-S@MCDs imparts astounding color-tunability which enables highly fluorescent fingerprint pattern developed on different non-porous surfaces along with immediate visual enhancement under UV-light, revealing a bright sharp fingerprint, along with long-time preservation of developed fingerprints. The creation and comparison of latent fingerprints (LFPs) are two key research in the recognition and detection of LFPs, respectively. In this work, developed fingerprints are regulated with an artificial intelligence program. The optimum sample has a very high degree of similarity with the standard control, as shown by the program's good matching score (86.94%) for the optimal sample. Hence, our results far outperform the benchmark attained using the conventional method, making the N-S@MCDs/corn-starch phosphors and the digital processing program suitable for use in real-world scenarios. | Humans; *Quantum Dots; Sweat; Artificial Intelligence; Forensic Anthropology; Powders; Dermatoglyphics; Algorithms; Coloring Agents; Machine Learning; Natural Resources; Starch; Carbon | PloS one | 2024 | 10.1371/journal.pone.0296270 [doi] e0296270 | https://pubmed.ncbi.nlm.nih.gov/38175842/ | not available | January 2024 |
39,309,215 | A deep learning method that identifies cellular heterogeneity using nanoscale nuclear features. | Carnevali D; Zhong L; Gonzalez-Almela E; Viana C; Rotkevich M; Wang A; Franco-Barranco D; Gonzalez-Marfil A; Neguembor MV; Castells-Garcia A; Arganda-Carreras I; Cosma MP | Cellular phenotypic heterogeneity is an important hallmark of many biological processes and understanding its origins remains a substantial challenge. This heterogeneity often reflects variations in the chromatin structure, influenced by factors such as viral infections and cancer, which dramatically reshape the cellular landscape. To address the challenge of identifying distinct cell states, we developed artificial intelligence of the nucleus (AINU), a deep learning method that can identify specific nuclear signatures at the nanoscale resolution. AINU can distinguish different cell states based on the spatial arrangement of core histone H3, RNA polymerase II or DNA from super-resolution microscopy images. With only a small number of images as the training data, AINU correctly identifies human somatic cells, human-induced pluripotent stem cells, very early stage infected cells transduced with DNA herpes simplex virus type 1 and even cancer cells after appropriate retraining. Finally, using AI interpretability methods, we find that the RNA polymerase II localizations in the nucleoli aid in distinguishing human-induced pluripotent stem cells from their somatic cells. Overall, AINU coupled with super-resolution microscopy of nuclear structures provides a robust tool for the precise detection of cellular heterogeneity, with considerable potential for advancing diagnostics and therapies in regenerative medicine, virology and cancer biology. | null | Nature machine intelligence | 2024 | 10.1038/s42256-024-00883-x [doi] | https://pubmed.ncbi.nlm.nih.gov/39309215/ | not available | January 2024 |
40,417,500 | MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answering. | Shi Y; Xu S; Yang T; Liu Z; Liu T; Li X; Liu N | Large Language Models (LLMs), although powerful in general domains, often perform poorly on domain-specific tasks such as medical question answering (QA). In addition, LLMs tend to function as "black-boxes", making it challenging to modify their behavior. To address the problem, our work employs a transparent process of retrieval augmented generation (RAG), aiming to improve LLM responses without the need for fine-tuning or retraining. Specifically, we propose a comprehensive retrieval strategy to extract medical facts from an external knowledge base, and then inject them into the LLM's query prompt. Focusing on medical QA, we evaluate the impact of different retrieval models and the number of facts on LLM performance using the MedQA-SMILE dataset. Notably, our retrieval-augmented Vicuna-7B model exhibited an accuracy improvement from 44.46% to 48.54%. This work underscores the potential of RAG to enhance LLM performance, offering a practical approach to mitigate the challenges posed by black-box LLMs. | *Information Storage and Retrieval/methods; Humans; *Natural Language Processing; *Knowledge Bases | AMIA ... Annual Symposium proceedings. AMIA Symposium | 2024 | null | https://pubmed.ncbi.nlm.nih.gov/40417500/ | not available | January 2024 |
37,132,145 | Current Scenario and Future Prospects of Adverse Drug Reactions (ADRs) Monitoring and Reporting Mechanisms in the Rural Areas of India. | Shukla S; Sharma P; Gupta P; Pandey S; Agrawal R; Rathour D; Kumar Kewat D; Singh R; Kumar Thakur S; Paliwal R; Sulakhiya K | Pharmacovigilance (PV) deals with the detection, collection, assessment, understanding, and prevention of adverse effects associated with drugs. The objective of PV is to ensure the safety of the medicines and patients by monitoring and reporting all adverse drug reactions (ADRs) associated with prescribed medicine usage. Findings have indicated that about 0.2- 24% of hospitalization cases are due to ADRs, of which 3.7% of patients have lethal ADRs. The reasons include the number of prescribed drugs, an increased number of new medicines in the market, an inadequate PV system for ADR monitoring, and a need for more awareness and knowledge about ADR reporting. Severe ADRs lead to enhanced hospital stays, increased treatment costs, risk of death, and many medical and economic consequences. Therefore, ADR reporting at its first instance is essential to avoid further harmful effects of the prescribed drugs. In India, the rate of ADR reporting is less than 1%, whereas worldwide, it is 5% due to a need for more awareness about PV and ADR monitoring among healthcare providers and patients. The main objective of this review is to highlight the current scenario and possible futuristic ways of ADR reporting methods in rural areas of India. We have searched the literature using PubMed, Google scholar, Indian citation index to retrieve the resources related to ADR monitoring and reporting in India's urban and rural areas. Spontaneous reporting is the most commonly used PV method to report ADRs in India's urban and rural areas. Evidence revealed that no effective ADR reporting mechanisms developed in rural areas causing underreporting of ADR, thus increasing the threat to the rural population. Hence, PV and ADR reporting awareness among healthcare professionals and patients, telecommunication, telemedicine, use of social media and electronic medical records, and artificial intelligence are the potential approaches for prevention, monitoring, and reporting of ADRs in rural areas. | Humans; *Artificial Intelligence; Adverse Drug Reaction Reporting Systems; Health Knowledge, Attitudes, Practice; *Drug-Related Side Effects and Adverse Reactions/diagnosis/epidemiology; India/epidemiology; Pharmacovigilance | Current drug safety | 2024 | 10.2174/1574886318666230428144120 [doi] | https://pubmed.ncbi.nlm.nih.gov/37132145/ | not available | January 2024 |
38,446,741 | Cracking the black box of deep sequence-based protein-protein interaction prediction. | Bernett J; Blumenthal DB; List M | Identifying protein-protein interactions (PPIs) is crucial for deciphering biological pathways. Numerous prediction methods have been developed as cheap alternatives to biological experiments, reporting surprisingly high accuracy estimates. We systematically investigated how much reproducible deep learning models depend on data leakage, sequence similarities and node degree information, and compared them with basic machine learning models. We found that overlaps between training and test sets resulting from random splitting lead to strongly overestimated performances. In this setting, models learn solely from sequence similarities and node degrees. When data leakage is avoided by minimizing sequence similarities between training and test set, performances become random. Moreover, baseline models directly leveraging sequence similarity and network topology show good performances at a fraction of the computational cost. Thus, we advocate that any improvements should be reported relative to baseline methods in the future. Our findings suggest that predicting PPIs remains an unsolved task for proteins showing little sequence similarity to previously studied proteins, highlighting that further experimental research into the 'dark' protein interactome and better computational methods are needed. | *Machine Learning | Briefings in bioinformatics | 2024 Jan 22 | 10.1093/bib/bbae076 [doi] bbae076 | https://pubmed.ncbi.nlm.nih.gov/38446741/ | not available | January 2024 |
38,042,100 | Exploring classical machine learning for identification of pathological lung auscultations. | Razvadauskas H; Vaiciukynas E; Buskus K; Arlauskas L; Nowaczyk S; Sadauskas S; Naudziunas A | The use of machine learning in biomedical research has surged in recent years thanks to advances in devices and artificial intelligence. Our aim is to expand this body of knowledge by applying machine learning to pulmonary auscultation signals. Despite improvements in digital stethoscopes and attempts to find synergy between them and artificial intelligence, solutions for their use in clinical settings remain scarce. Physicians continue to infer initial diagnoses with less sophisticated means, resulting in low accuracy, leading to suboptimal patient care. To arrive at a correct preliminary diagnosis, the auscultation diagnostics need to be of high accuracy. Due to the large number of auscultations performed, data availability opens up opportunities for more effective sound analysis. In this study, digital 6-channel auscultations of 45 patients were used in various machine learning scenarios, with the aim of distinguishing between normal and abnormal pulmonary sounds. Audio features (such as fundamental frequencies F0-4, loudness, HNR, DFA, as well as descriptive statistics of log energy, RMS and MFCC) were extracted using the Python library Surfboard. Windowing, feature aggregation, and concatenation strategies were used to prepare data for machine learning algorithms in unsupervised (fair-cut forest, outlier forest) and supervised (random forest, regularized logistic regression) settings. The evaluation was carried out using 9-fold stratified cross-validation repeated 30 times. Decision fusion by averaging the outputs for a subject was also tested and found to be helpful. Supervised models showed a consistent advantage over unsupervised ones, with random forest achieving a mean AUC ROC of 0.691 (accuracy 71.11%, Kappa 0.416, F1-score 0.675) in side-based detection and a mean AUC ROC of 0.721 (accuracy 68.89%, Kappa 0.371, F1-score 0.650) in patient-based detection. | Humans; *Artificial Intelligence; *Auscultation/methods; Algorithms; Machine Learning; Lung | Computers in biology and medicine | 2024 Jan | S0010-4825(23)01249-0 [pii] 10.1016/j.compbiomed.2023.107784 [doi] | https://pubmed.ncbi.nlm.nih.gov/38042100/ | not available | January 2024 |
38,260,726 | Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions. | Dixit S; Kumar A; Srinivasan K; Vincent PMDR; Ramu Krishnan N | Clustered regularly interspaced short palindromic repeat (CRISPR)-based genome editing (GED) technologies have unlocked exciting possibilities for understanding genes and improving medical treatments. On the other hand, Artificial intelligence (AI) helps genome editing achieve more precision, efficiency, and affordability in tackling various diseases, like Sickle cell anemia or Thalassemia. AI models have been in use for designing guide RNAs (gRNAs) for CRISPR-Cas systems. Tools like DeepCRISPR, CRISTA, and DeepHF have the capability to predict optimal guide RNAs (gRNAs) for a specified target sequence. These predictions take into account multiple factors, including genomic context, Cas protein type, desired mutation type, on-target/off-target scores, potential off-target sites, and the potential impacts of genome editing on gene function and cell phenotype. These models aid in optimizing different genome editing technologies, such as base, prime, and epigenome editing, which are advanced techniques to introduce precise and programmable changes to DNA sequences without relying on the homology-directed repair pathway or donor DNA templates. Furthermore, AI, in collaboration with genome editing and precision medicine, enables personalized treatments based on genetic profiles. AI analyzes patients' genomic data to identify mutations, variations, and biomarkers associated with different diseases like Cancer, Diabetes, Alzheimer's, etc. However, several challenges persist, including high costs, off-target editing, suitable delivery methods for CRISPR cargoes, improving editing efficiency, and ensuring safety in clinical applications. This review explores AI's contribution to improving CRISPR-based genome editing technologies and addresses existing challenges. It also discusses potential areas for future research in AI-driven CRISPR-based genome editing technologies. The integration of AI and genome editing opens up new possibilities for genetics, biomedicine, and healthcare, with significant implications for human health. | null | Frontiers in bioengineering and biotechnology | 2023 | 10.3389/fbioe.2023.1335901 [doi] 1335901 | https://pubmed.ncbi.nlm.nih.gov/38260726/ | not available | January 2024 |
37,953,597 | Effects of Mitigation and Control Policies in Realistic Epidemic Models Accounting for Household Transmission Dynamics. | Alarid-Escudero F; Andrews JR; Goldhaber-Fiebert JD | BACKGROUND: Compartmental infectious disease (ID) models are often used to evaluate nonpharmaceutical interventions (NPIs) and vaccines. Such models rarely separate within-household and community transmission, potentially introducing biases in situations in which multiple transmission routes exist. We formulated an approach that incorporates household structure into ID models, extending the work of House and Keeling. DESIGN: We developed a multicompartment susceptible-exposed-infectious-recovered-susceptible-vaccinated (MC-SEIRSV) modeling framework, allowing nonexponentially distributed duration in exposed and infectious compartments, that tracks within-household and community transmission. We simulated epidemics that varied by community and household transmission rates, waning immunity rate, household size (3 or 5 members), and numbers of exposed and infectious compartments (1-3 each). We calibrated otherwise identical models without household structure to the early phase of each parameter combination's epidemic curve. We compared each model pair in terms of epidemic forecasts and predicted NPI and vaccine impacts on the timing and magnitude of the epidemic peak and its total size. Meta-analytic regressions characterized the relationship between household structure inclusion and the size and direction of biases. RESULTS: Otherwise similar models with and without household structure produced equivalent early epidemic curves. However, forecasts from models without household structure were biased. Without intervention, they were upward biased on peak size and total epidemic size, with biases also depending on the number of exposed and infectious compartments. Model-estimated NPI effects of a 60% reduction in community contacts on peak time and size were systematically overestimated without household structure. Biases were smaller with a 20% reduction NPI. Because vaccination affected both community and household transmission, their biases were smaller. CONCLUSIONS: ID models without household structure can produce biased outcomes in settings in which within-household and community transmission differ. HIGHLIGHTS: Infectious disease models rarely separate household transmission from community transmission. The pace of household transmission may differ from community transmission, depends on household size, and can accelerate epidemic growth.Many infectious disease models assume exponential duration distributions for infected states. However, the duration of most infections is not exponentially distributed, and distributional choice alters modeled epidemic dynamics and intervention effectiveness.We propose a mathematical framework for household and community transmission that allows for nonexponential duration times and a suite of interventions and quantified the effect of accounting for household transmission by varying household size and duration distributions of infected states on modeled epidemic dynamics.Failure to include household structure induces biases in the modeled overall course of an epidemic and the effects of interventions delivered differentially in community settings. Epidemic dynamics are faster and more intense in populations with larger household sizes and for diseases with nonexponentially distributed infectious durations. Modelers should consider explicitly incorporating household structure to quantify the effects of non-pharmaceutical interventions (e.g., shelter-in-place). | Humans; *Communicable Diseases/epidemiology; *Epidemics/prevention & control | Medical decision making : an international journal of the Society for Medical Decision Making | 2024 Jan | 10.1177/0272989X231205565 [doi] | https://pubmed.ncbi.nlm.nih.gov/37953597/ | not available | January 2024 |
38,245,517 | A magnetic multi-layer soft robot for on-demand targeted adhesion. | Chen Z; Wang Y; Chen H; Law J; Pu H; Xie S; Duan F; Sun Y; Liu N; Yu J | Magnetic soft robots have shown great potential for biomedical applications due to their high shape reconfigurability, motion agility, and multi-functionality in physiological environments. Magnetic soft robots with multi-layer structures can enhance the loading capacity and function complexity for targeted delivery. However, the interactions between soft entities have yet to be fully investigated, and thus the assembly of magnetic soft robots with on-demand motion modes from multiple film-like layers is still challenging. Herein, we model and tailor the magnetic interaction between soft film-like layers with distinct in-plane structures, and then realize multi-layer soft robots that are capable of performing agile motions and targeted adhesion. Each layer of the robot consists of a soft magnetic substrate and an adhesive film. The mechanical properties and adhesion performance of the adhesive films are systematically characterized. The robot is capable of performing two locomotion modes, i.e., translational motion and tumbling motion, and also the on-demand separation with one side layer adhered to tissues. Simulation results are presented, which have a good qualitative agreement with the experimental results. The feasibility of using the robot to perform multi-target adhesion in a stomach is validated in both ex-vivo and in-vivo experiments. | Humans; *Robotics; Physical Phenomena; Motion; Computer Simulation; Tissue Adhesions; Magnetic Phenomena | Nature communications | 2024 Jan 20 | 10.1038/s41467-024-44995-9 [doi] 644 | https://pubmed.ncbi.nlm.nih.gov/38245517/ | not available | January 2024 |
37,501,646 | Chatbot GPT can be grossly inaccurate. | Diamandis EP | null | null | Clinical chemistry and laboratory medicine | 2024 Jan 26 | 10.1515/cclm-2023-0765 [doi] | https://pubmed.ncbi.nlm.nih.gov/37501646/ | not available | January 2024 |
38,228,843 | Influence of periodic pulse intake on the ventilation efficiency of positive pressure explosion-proof robot. | Fang M; Chu X; Yu L; Fang Y; Hou L; Cheng X; Wang J | The ventilation work is an important step to be completed before the start of the positive pressure explosion-proof robot. The existing explosion-proof technology uses constant pressure inflation, which will cause explosive gas to accumulate in the corner area of the cavity for a long time. In order to solve this problem, a ventilation method with periodic pulse intake is proposed. Based on the finite element method, the cleaning and ventilation process of the positive pressure explosion-proof robot is simulated and analyzed. The concentration of explosive gas in the robot cavity with time under constant pressure intake and pulse intake with different periods and amplitudes is compared. The simulation results show that the pulse intake is beneficial to the ventilation of the corner position. The period and amplitude of the pulse intake has an effect on the ventilation efficiency, when the period is the same, the greater the amplitude of the pulse intake, the higher the ventilation efficiency; when the amplitude is the same, the smaller the period of the pulse intake, the higher the ventilation efficiency. After experimental verification, the validity of the simulation results is proved. This study helps to improve the ventilation efficiency of positive-pressure explosion-proof robots and provides guidance for practical applications. | null | Scientific reports | 2024 Jan 16 | 10.1038/s41598-024-52011-9 [doi] 1433 | https://pubmed.ncbi.nlm.nih.gov/38228843/ | not available | January 2024 |
39,226,701 | Joint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders. | Ji Y; Silva RF; Adali T; Wen X; Zhu Q; Jiang R; Zhang D; Qi S; Calhoun VD | Identifying biomarkers for computer-aided diagnosis (CAD) is crucial for early intervention of psychiatric disorders. Multi-site data have been utilized to increase the sample size and improve statistical power, while multi-modality classification offers significant advantages over traditional single-modality based approaches for diagnosing psychiatric disorders. However, inter-site heterogeneity and intra-modality heterogeneity present challenges to multi-site and multi-modality based classification. In this paper, brain functional and structural networks (BFNs/BSNs) from multiple sites were constructed to establish a joint multi-site multi-modality framework for psychiatric diagnosis. To do this we developed a hypergraph based multi-source domain adaptation (HMSDA) which allowed us to transform source domain subjects into a target domain. A local ordinal structure based multi-task feature selection (LOSMFS) approach was developed by integrating the transformed functional and structural connections (FCs/SCs). The effectiveness of our method was validated by evaluating diagnosis of both schizophrenia (SZ) and autism spectrum disorder (ASD). The proposed method obtained accuracies of 92.2 %+/-2.22 % and 84.8 %+/-2.68 % for the diagnosis of SZ and ASD, respectively. We also compared with 6 DA, 10 multi-modality feature selection, and 8 multi-site and multi-modality methods. Results showed the proposed HMSDA+LOSMFS effectively integrated multi-site and multi-modality data to enhance psychiatric diagnosis and identify disorder-specific diagnostic brain connections. | Humans; Male; Female; Adult; *Schizophrenia/diagnosis; *Magnetic Resonance Imaging/methods; Autism Spectrum Disorder/diagnosis; Brain/physiopathology/diagnostic imaging; Young Adult; Mental Disorders/diagnosis; Adolescent; Diagnosis, Computer-Assisted/methods | NeuroImage. Clinical | 2024 | S2213-1582(24)00102-5 [pii] 10.1016/j.nicl.2024.103663 [doi] 103663 | https://pubmed.ncbi.nlm.nih.gov/39226701/ | not available | January 2024 |
37,930,259 | Machine Learning in Spine Surgery: A Narrative Review. | Adida S; Legarreta AD; Hudson JS; McCarthy D; Andrews E; Shanahan R; Taori S; Lavadi RS; Buell TJ; Hamilton DK; Agarwal N; Gerszten PC | Artificial intelligence and machine learning (ML) can offer revolutionary advances in their application to the field of spine surgery. Within the past 5 years, novel applications of ML have assisted in surgical decision-making, intraoperative imaging and navigation, and optimization of clinical outcomes. ML has the capacity to address many different clinical needs and improve diagnostic and surgical techniques. This review will discuss current applications of ML in the context of spine surgery by breaking down its implementation preoperatively, intraoperatively, and postoperatively. Ethical considerations to ML and challenges in ML implementation must be addressed to maximally benefit patients, spine surgeons, and the healthcare system. Areas for future research in augmented reality and mixed reality, along with limitations in generalizability and bias, will also be highlighted. | Humans; *Artificial Intelligence; Machine Learning; Spine/surgery; *Surgeons | Neurosurgery | 2024 Jan 1 | 10.1227/neu.0000000000002660 [doi] | https://pubmed.ncbi.nlm.nih.gov/37930259/ | not available | January 2024 |
39,520,166 | Pneumonia detection on chest X-rays from Xception-based transfer learning and logistic regression. | Mujahid M; Rustam F; Chakrabarti P; Mallampati B; de la Torre Diez I; Gali P; Chunduri V; Ashraf I | Pneumonia is a dangerous disease that kills millions of children and elderly patients worldwide every year. The detection of pneumonia from a chest x-ray is perpetrated by expert radiologists. The chest x-ray is cheaper and is most often used to diagnose pneumonia. However, chest x-ray-based diagnosis requires expert radiologists which is time-consuming and laborious. Moreover, COVID-19 and pneumonia have similar symptoms which leads to false positives. Machine learning-based solutions have been proposed for the automatic prediction of pneumonia from chest X-rays, however, such approaches lack robustness and high accuracy due to data imbalance and generalization errors. This study focuses on elevating the performance of machine learning models by dealing with data imbalanced problems using data augmentation. Contrary to traditional machine learning models that required hand-crafted features, this study uses transfer learning for automatic feature extraction using Xception and VGG-16 to train classifiers like support vector machine, logistic regression, K nearest neighbor, stochastic gradient descent, extra tree classifier, and gradient boosting machine. Experiments involve the use of hand-crafted features, as well as, transfer learning-based feature extraction for pneumonia detection. Performance comparison using Xception and VGG-16 features suggest that transfer learning-based features tend to show better performance than hand-crafted features and an accuracy of 99.23% can be obtained for pneumonia using chest X-rays. | Humans; *Pneumonia/diagnostic imaging/diagnosis; *COVID-19/diagnostic imaging; *Machine Learning; Logistic Models; Radiography, Thoracic/methods; Support Vector Machine; SARS-CoV-2 | Technology and health care : official journal of the European Society for Engineering and Medicine | 2024 | 10.3233/THC-230313 [doi] | https://pubmed.ncbi.nlm.nih.gov/39520166/ | not available | January 2024 |
37,482,489 | Prognostic Value of PSMA PET/CT in Prostate Cancer. | Lawal IO; Ndlovu H; Kgatle M; Mokoala KMG; Sathekge MM | Prostate-specific membrane antigen (PSMA) is a transmembrane glycoprotein expressed in the majority of prostate cancer (PCa). PSMA has an enzymatic function that makes metabolic substrates such as folate available for utilization by PCa cells. Intracellular folate availability drives aggressive tumor phenotype. PSMA expression is, therefore, a marker of aggressive tumor biology. The large extracellular domain of PSMA is available for targeting by diagnostic and therapeutic radionuclides, making it a suitable cellular epitope for theranostics. PET imaging of radiolabeled PSMA ligands has several prognostic utilities. In the prebiopsy setting, intense PSMA avidity in a prostate lesion correlate well with clinically significant PCa (csPCa) on histology. When used for staging, PSMA PET imaging outperforms conventional imaging for the accurate staging of primary PCa, and findings on imaging predict post-treatment outcomes. The biggest contribution of PSMA PET imaging to PCa management is in the biochemical recurrence setting, where it has emerged as the most sensitive imaging modality for the localization of PCa recurrence by helping to guide salvage therapy. PSMA PET obtained for localizing the site of recurrence is prognostic, such that a higher lesion number predicts a less favorable outcome to salvage radiotherapy or surgical intervention. Systemic therapy is given to patients with advanced PCa with distant metastasis. PSMA PET is useful for predicting response to treatments with chemotherapy, first- and second-line androgen deprivation therapies, and PSMA-targeted radioligand therapy. Artificial intelligence using machine learning algorithms allows for the mining of information from clinical images not visible to the human eyes. Artificial intelligence applied to PSMA PET images, therefore, holds great promise for prognostication in PCa management. | Male; Humans; *Prostatic Neoplasms/pathology; Positron Emission Tomography Computed Tomography/methods; Prognosis; Androgen Antagonists; Artificial Intelligence; Positron-Emission Tomography; Folic Acid; Gallium Radioisotopes | Seminars in nuclear medicine | 2024 Jan | S0001-2998(23)00058-2 [pii] 10.1053/j.semnuclmed.2023.07.003 [doi] | https://pubmed.ncbi.nlm.nih.gov/37482489/ | not available | January 2024 |
37,802,675 | Advancements in Positron Emission Tomography Detectors: From Silicon Photomultiplier Technology to Artificial Intelligence Applications. | Lee JS; Lee MS | This review article focuses on PET detector technology, which is the most crucial factor in determining PET image quality. The article highlights the desired properties of PET detectors, including high detection efficiency, spatial resolution, energy resolution, and timing resolution. Recent advancements in PET detectors to improve these properties are also discussed, including the use of silicon photomultiplier technology, advancements in depth-of-interaction and time-of-flight PET detectors, and the use of artificial intelligence for detector development. The article provides an overview of PET detector technology and its recent advancements, which can significantly enhance PET image quality. | Humans; *Artificial Intelligence; *Positron-Emission Tomography/methods; Technology | PET clinics | 2024 Jan | S1556-8598(23)00059-7 [pii] 10.1016/j.cpet.2023.06.003 [doi] | https://pubmed.ncbi.nlm.nih.gov/37802675/ | not available | January 2024 |
37,933,852 | DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options. | Basu S; Zhao B; Biro B; Faraggi E; Gsponer J; Hu G; Kloczkowski A; Malhis N; Mirdita M; Soding J; Steinegger M; Wang D; Wang K; Xu D; Zhang J; Kurgan L | The DescribePROT database of amino acid-level descriptors of protein structures and functions was substantially expanded since its release in 2020. This expansion includes substantial increase in the size, scope, and quality of the underlying data, the addition of experimental structural information, the inclusion of new data download options, and an upgraded graphical interface. DescribePROT currently covers 19 structural and functional descriptors for proteins in 273 reference proteomes generated by 11 accurate and complementary predictive tools. Users can search our resource in multiple ways, interact with the data using the graphical interface, and download data at various scales including individual proteins, entire proteomes, and whole database. The annotations in DescribePROT are useful for a broad spectrum of studies that include investigations of protein structure and function, development and validation of predictive tools, and to support efforts in understanding molecular underpinnings of diseases and development of therapeutics. DescribePROT can be freely accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/. | *Proteome/chemistry; Databases, Factual; *Amino Acids | Nucleic acids research | 2024 Jan 5 | 10.1093/nar/gkad985 [doi] | https://pubmed.ncbi.nlm.nih.gov/37933852/ | not available | January 2024 |
40,417,484 | A Comprehensive System for Searching and Evaluating Genomic Variant Evidence Using AI and Knowledge Bases to Support Personalized Medicine. | Wang J; Li H; Liu H | We introduce an innovative automated system for the search and assessment of genetic variant evidence, meticulously aligned with ACMG guidelines. Leveraging the synergistic power of artificial intelligence (AI), elastic search, and an extensive knowledge base, our system advances the efficiency and accuracy of genetic variant interpretation. Distinct from existing methodologies, it features a pioneering literature filtering mechanism that automates the identification and relevance ranking of scientific articles, significantly reducing the time spending on literature evidence search and optimizing the evidence assessment process. Implemented and rigorously tested by a commercial company hereditary cancer variant curation team, the system demonstrated its effectiveness and scalability by processing over 3 million PMIDs and 1.8 million full-text articles. Throughout the period of active utilization, significant insights were gleaned into the real-world impact and user experience of the system, conclusively affirming its robustness. Our comparative analysis with Mastermind 2.0 highlights the system's enhanced performance in minimizing false positives for various mutation types. The core AI model exhibits exceptional precision, recall, and F1 scores above 0.8, signifying its adeptness in selecting pertinent literature for variant classification. The experience and knowledge acquired from deploying the system in a commercial setting provide a distinctive outlook on its practicality and prospects for future development. The novel integration of AI with traditional genetic variant curation processes heralds a new era in the field, promising significant advancements and broader application prospects. | *Artificial Intelligence; *Precision Medicine; *Knowledge Bases; Humans; *Genetic Variation; Genomics | AMIA ... Annual Symposium proceedings. AMIA Symposium | 2024 | null | https://pubmed.ncbi.nlm.nih.gov/40417484/ | not available | January 2024 |
38,370,422 | Graph convolutional networks for automated intracranial artery labeling. | Vos IN; Ruigrok YM; Bhat IR; Timmins KM; Velthuis BK; Kuijf HJ | PURPOSE: Unruptured intracranial aneurysms (UIAs) can cause aneurysmal subarachnoid hemorrhage, a severe and often lethal type of stroke. Automated labeling of intracranial arteries can facilitate the identification of risk factors associated with UIAs. This study aims to improve intracranial artery labeling using atlas-based features in graph convolutional networks. APPROACH: We included three-dimensional time-of-flight magnetic resonance angiography scans from 150 individuals. Two widely used graph convolutional operators, GCNConv and GraphConv, were employed in models trained to classify 12 bifurcations of interest. Cross-validation was applied to explore the effectiveness of atlas-based features in node classification. The results were tested for statistically significant differences using a Wilcoxon signed-rank test. Model repeatability and calibration were assessed on the test set for both operators. In addition, we evaluated model interpretability and node feature contribution using explainable artificial intelligence. RESULTS: Atlas-based features led to statistically significant improvements in node classification (p < 0.05). The results showed that the best discrimination and calibration performances were obtained using the GraphConv operator, which yielded a mean recall of 0.87, precision of 0.90, and expected calibration error of 0.02. CONCLUSIONS: The addition of atlas-based features improved node classification results. The GraphConv operator, which incorporates higher-order structural information during training, is recommended over the GCNConv operator based on the accuracy and calibration of predicted outcomes. | null | Journal of medical imaging (Bellingham, Wash.) | 2024 Jan | 10.1117/1.JMI.11.1.014007 [doi] 014007 | https://pubmed.ncbi.nlm.nih.gov/38370422/ | not available | January 2024 |
38,270,726 | Convolutional neural networks for the differentiation between benign and malignant renal tumors with a multicenter international computed tomography dataset. | Klontzas ME; Kalarakis G; Koltsakis E; Papathomas T; Karantanas AH; Tzortzakakis A | OBJECTIVES: To use convolutional neural networks (CNNs) for the differentiation between benign and malignant renal tumors using contrast-enhanced CT images of a multi-institutional, multi-vendor, and multicenter CT dataset. METHODS: A total of 264 histologically confirmed renal tumors were included, from US and Swedish centers. Images were augmented and divided randomly 70%:30% for algorithm training and testing. Three CNNs (InceptionV3, Inception-ResNetV2, VGG-16) were pretrained with transfer learning and fine-tuned with our dataset to distinguish between malignant and benign tumors. The ensemble consensus decision of the three networks was also recorded. Performance of each network was assessed with receiver operating characteristics (ROC) curves and their area under the curve (AUC-ROC). Saliency maps were created to demonstrate the attention of the highest performing CNN. RESULTS: Inception-ResNetV2 achieved the highest AUC of 0.918 (95% CI 0.873-0.963), whereas VGG-16 achieved an AUC of 0.813 (95% CI 0.752-0.874). InceptionV3 and ensemble achieved the same performance with an AUC of 0.894 (95% CI 0.844-0.943). Saliency maps indicated that Inception-ResNetV2 decisions are based on the characteristics of the tumor while in most tumors considering the characteristics of the interface between the tumor and the surrounding renal parenchyma. CONCLUSION: Deep learning based on a diverse multicenter international dataset can enable accurate differentiation between benign and malignant renal tumors. CRITICAL RELEVANCE STATEMENT: Convolutional neural networks trained on a diverse CT dataset can accurately differentiate between benign and malignant renal tumors. KEY POINTS: * Differentiation between benign and malignant tumors based on CT is extremely challenging. * Inception-ResNetV2 trained on a diverse dataset achieved excellent differentiation between tumor types. * Deep learning can be used to distinguish between benign and malignant renal tumors. | null | Insights into imaging | 2024 Jan 25 | 10.1186/s13244-023-01601-8 [doi] 26 | https://pubmed.ncbi.nlm.nih.gov/38270726/ | not available | January 2024 |
37,862,115 | Generative Artificial Intelligence Tools in Medicine Will Amplify Stigmatizing Language. | Tate S | null | null | Journal of addiction medicine | 2024 Jan-Feb 01 | 10.1097/ADM.0000000000001237 [doi] | https://pubmed.ncbi.nlm.nih.gov/37862115/ | not available | January 2024 |
38,047,362 | Artificial Intelligence for the Management of Breast Cancer: An Overview. | Gandhi H; Kumar K | Breast cancer is a severe global health problem, and early detection, accurate diagnosis, and personalized treatment is the key to improving patient outcomes. Artificial intelligence (AI) and machine learning (ML) have emerged as promising breast cancer research and clinical practice tools in recent years. Various projects are underway in early detection, diagnosis, prognosis, drug discovery, advanced image analysis, precision medicine, predictive modeling, and personalized treatment planning using artificial intelligence and machine learning. These projects use different algorithms, including convolutional neural networks (CNNs), support vector machines (SVMs), decision trees, and deep learning methods, to analyze and improve different types of data, such as clinical, genomic, and imaging data for breast cancer management. The success of these projects has the potential to transform breast cancer care, and continued research and development in this area is likely to lead to more accurate and personalized breast cancer diagnosis, treatment, and outcomes. | Humans; *Breast Neoplasms/therapy/diagnosis/diagnostic imaging; Female; *Artificial Intelligence; Precision Medicine/methods; Machine Learning; Neural Networks, Computer; Drug Discovery/methods | Current drug discovery technologies | 2024 | 10.2174/0115701638262066231030052520 [doi] | https://pubmed.ncbi.nlm.nih.gov/38047362/ | not available | January 2024 |
38,701,156 | IMPatienT: An Integrated Web Application to Digitize, Process and Explore Multimodal PATIENt daTa. | Meyer C; Romero NB; Evangelista T; Cadot B; Laporte J; Jeannin-Girardon A; Collet P; Ayadi A; Chennen K; Poch O | Medical acts, such as imaging, lead to the production of various medical text reports that describe the relevant findings. This induces multimodality in patient data by combining image data with free-text and consequently, multimodal data have become central to drive research and improve diagnoses. However, the exploitation of patient data is problematic as the ecosystem of analysis tools is fragmented according to the type of data (images, text, genetics), the task (processing, exploration) and domain of interest (clinical phenotype, histology). To address the challenges, we developed IMPatienT (Integrated digital Multimodal PATIENt daTa), a simple, flexible and open-source web application to digitize, process and explore multimodal patient data. IMPatienT has a modular architecture allowing to: (i) create a standard vocabulary for a domain, (ii) digitize and process free-text data, (iii) annotate images and perform image segmentation, (iv) generate a visualization dashboard and provide diagnosis decision support. To demonstrate the advantages of IMPatienT, we present a use case on a corpus of 40 simulated muscle biopsy reports of congenital myopathy patients. As IMPatienT provides users with the ability to design their own vocabulary, it can be adapted to any research domain and can be used as a patient registry for exploratory data analysis. A demo instance of the application is available at https://impatient.lbgi.fr/. | Humans; *Internet; Software | Journal of neuromuscular diseases | 2024 | 10.3233/JND-230085 [doi] | https://pubmed.ncbi.nlm.nih.gov/38701156/ | not available | January 2024 |
38,192,329 | Can Large Language Models Generate Outpatient Clinic Letters at First Consultation That Incorporate Complication Profiles From UK and USA Aesthetic Plastic Surgery Associations? | Roberts RHR; Ali SR; Dobbs TD; Whitaker IS | The importance of written communication between clinicians and patients, especially in the wake of the Supreme Court case of Montgomery vs Lanarkshire, has led to a shift toward patient-centric care in the United Kingdom. This study investigates the use of large language models (LLMs) like ChatGPT and Google Bard in enhancing clinic letters with gold-standard complication profiles, aiming to improve patients' understanding and save clinicians' time in aesthetic plastic surgery. The aim of this study is to assess the effectiveness of LLMs in integrating complication profiles from authoritative sources into clinic letters, thus enhancing patient comprehension and clinician efficiency in aesthetic plastic surgery. Seven widely performed aesthetic procedures were chosen, and complication profiles were sourced from the British Association of Aesthetic Plastic Surgeons (BAAPS) and the American Society of Plastic Surgeons (ASPS). We evaluated the proficiency of the ChatGPT4, ChatGPT3.5, and Google Bard in generating clinic letters which incorporated complication profiles from online resources. These letters were assessed for readability using an online tool, targeting a recommended sixth-grade reading level. ChatGPT4 achieved the highest compliance in integrating complication profiles from BAAPS and ASPS websites, with average readability grades between eighth and ninth. ChatGPT3.5 and Google Bard showed lower compliance, particularly when accessing paywalled content like the ASPS Informed Consent Bundle. In conclusion, LLMs, particularly ChatGPT4, show promise in enhancing patient communications in aesthetic plastic surgery by effectively incorporating standard complication profiles into clinic letters. This aids in informed decision making and time saving for clinicians. However, the study underscores the need for improvements in data accessibility, search capabilities, and ethical considerations for optimal LLM integration into healthcare communications. Future enhancements should focus on better interpretation of inaccessible formats and a Human in the Loop approach to combine Artifical Intelligence capabilities with clinician expertise. | null | Aesthetic surgery journal. Open forum | 2024 | 10.1093/asjof/ojad109 [doi] ojad109 | https://pubmed.ncbi.nlm.nih.gov/38192329/ | not available | January 2024 |
38,260,055 | Development and deployment of a nationwide predictive model for chronic kidney disease progression in diabetic patients. | Fu Z; Wang Z; Clemente K; Jaisinghani M; Poon KMT; Yeo AWT; Ang GL; Liew A; Lim CK; Foo MWY; Chow WL; Ta WA | AIM: Chronic kidney disease (CKD) is a major complication of diabetes and a significant disease burden on the healthcare system. The aim of this work was to apply a predictive model to identify high-risk patients in the early stages of CKD as a means to provide early intervention to avert or delay kidney function deterioration. MATERIALS AND METHODS: Using the data from the National Diabetes Database in Singapore, we applied a machine-learning algorithm to develop a predictive model for CKD progression in diabetic patients and to deploy the model nationwide. RESULTS: Our model was rigorously validated. It outperformed existing models and clinician predictions. The area under the receiver operating characteristic curve (AUC) of our model is 0.88, with the 95% confidence interval being 0.87 to 0.89. In recognition of its higher and consistent accuracy and clinical usefulness, our CKD model became the first clinical model deployed nationwide in Singapore and has been incorporated into a national program to engage patients in long-term care plans in battling chronic diseases. The risk score generated by the model stratifies patients into three risk levels, which are embedded into the Diabetes Patient Dashboard for clinicians and care managers who can then allocate healthcare resources accordingly. CONCLUSION: This project provided a successful example of how an artificial intelligence (AI)-based model can be adopted to support clinical decision-making nationwide. | null | Frontiers in nephrology | 2023 | 10.3389/fneph.2023.1237804 [doi] 1237804 | https://pubmed.ncbi.nlm.nih.gov/38260055/ | not available | January 2024 |
37,633,496 | Comment on "ChatGPT Answers Common Patient Questions About Colonoscopy". | Wu Q | null | Humans; *Colonoscopy; *Artificial Intelligence | Gastroenterology | 2024 Jan | S0016-5085(23)04913-2 [pii] 10.1053/j.gastro.2023.08.030 [doi] | https://pubmed.ncbi.nlm.nih.gov/37633496/ | not available | January 2024 |
39,559,784 | Optimized Clinical Feature Analysis for Improved Cardiovascular Disease Risk Screening. | Vyshnya S; Epperson R; Giuste F; Shi W; Hornback A; Wang MD | Objective: To develop a clinical decision support tool that can predict cardiovascular disease (CVD) risk with high accuracy while requiring minimal clinical feature input, thus reducing the time and effort required by clinicians to manually enter data prior to obtaining patient risk assessment. Results: In this study, we propose a robust feature selection approach that identifies five key features strongly associated with CVD risk, which have been found to be consistent across various models. The machine learning model developed using this optimized feature set achieved state-of-the-art results, with an AUROC of 91.30%, sensitivity of 89.01%, and specificity of 85.39%. Furthermore, the insights obtained from explainable artificial intelligence techniques enable medical practitioners to offer personalized interventions by prioritizing patient-specific high-risk factors. Conclusion: Our work illustrates a robust approach to patient risk prediction which minimizes clinical feature requirements while also generating patient-specific insights to facilitate shared decision-making between clinicians and patients. | null | IEEE open journal of engineering in medicine and biology | 2024 | 10.1109/OJEMB.2023.3347479 [doi] | https://pubmed.ncbi.nlm.nih.gov/39559784/ | not available | January 2024 |
38,172,095 | AI co-pilot bronchoscope robot. | Zhang J; Liu L; Xiang P; Fang Q; Nie X; Ma H; Hu J; Xiong R; Wang Y; Lu H | The unequal distribution of medical resources and scarcity of experienced practitioners confine access to bronchoscopy primarily to well-equipped hospitals in developed regions, contributing to the unavailability of bronchoscopic services in underdeveloped areas. Here, we present an artificial intelligence (AI) co-pilot bronchoscope robot that empowers novice doctors to conduct lung examinations as safely and adeptly as experienced colleagues. The system features a user-friendly, plug-and-play catheter, devised for robot-assisted steering, facilitating access to bronchi beyond the fifth generation in average adult patients. Drawing upon historical bronchoscopic videos and expert imitation, our AI-human shared control algorithm enables novice doctors to achieve safe steering in the lung, mitigating misoperations. Both in vitro and in vivo results underscore that our system equips novice doctors with the skills to perform lung examinations as expertly as seasoned practitioners. This study offers innovative strategies to address the pressing issue of medical resource disparities through AI assistance. | Adult; Humans; Artificial Intelligence; Bronchoscopes; *Bronchoscopy/instrumentation/methods; *Robotics | Nature communications | 2024 Jan 4 | 10.1038/s41467-023-44385-7 [doi] 241 | https://pubmed.ncbi.nlm.nih.gov/38172095/ | not available | January 2024 |
38,199,790 | Feasibility of an artificial intelligence phone call for postoperative care following cataract surgery in a diverse population: two phase prospective study protocol. | Hatamnejad A; Higham A; Somani S; Tam ES; Lim E; Khavandi S; de Pennington N; Chiu HH | INTRODUCTION: Artificial intelligence (AI) development has led to improvements in many areas of medicine. Canada has workforce pressures in delivering cataract care. A potential solution is using AI technology that can automate care delivery, increase effectiveness and decrease burdens placed on patients and the healthcare system. This study assesses the use of 'Dora', an example of an AI assistant that is able to deliver a regulated autonomous, voice-based, natural-language consultation with patients over the telephone. Dora is used in routine practice in the UK, but this study seeks to assess the safety, usability, acceptability and cost-effectiveness of using the technology in Canada. METHODS AND ANALYSIS: This is a two-phase prospective single-centred trial. An expected 250 patients will be recruited for each phase of the study. For Phase I of the study, Dora will phone patients at postoperative week 1 and for Phase II of the study, Dora will phone patients within 24hours of their cataract surgery and again at postoperative week 1. We will evaluate the agreement between Dora and a supervising clinician regarding the need for further review based on the patients' symptoms. A random sample of patients will undergo the System Usability Scale followed by an extended semi-structured interview. The primary outcome of agreement between Dora and the supervisor will be assessed using the kappa statistic. Qualitative data from the interviews will further gauge patient opinions about Dora's usability, appropriateness and level of satisfaction. ETHICS AND DISSEMINATION: Research Ethics Board William Osler Health System (ID: 22-0044) has approved this study and will be conducted by guidelines of Declaration of Helsinki. Master-linking sheet will contain the patient chart identification (ID), full name, date of birth and study ID. Results will be shared through peer-reviewed journals and presentations at conferences. | Humans; Prospective Studies; Postoperative Care; *Artificial Intelligence; Feasibility Studies; *Cataract | BMJ open ophthalmology | 2024 Jan 10 | 10.1136/bmjophth-2023-001475 [doi] e001475 | https://pubmed.ncbi.nlm.nih.gov/38199790/ | not available | January 2024 |
38,166,722 | A newly developed deep learning-based system for automatic detection and classification of small bowel lesions during double-balloon enteroscopy examination. | Zhu Y; Lyu X; Tao X; Wu L; Yin A; Liao F; Hu S; Wang Y; Zhang M; Huang L; Wang J; Zhang C; Gong D; Jiang X; Zhao L; Yu H | BACKGROUND: Double-balloon enteroscopy (DBE) is a standard method for diagnosing and treating small bowel disease. However, DBE may yield false-negative results due to oversight or inexperience. We aim to develop a computer-aided diagnostic (CAD) system for the automatic detection and classification of small bowel abnormalities in DBE. DESIGN AND METHODS: A total of 5201 images were collected from Renmin Hospital of Wuhan University to construct a detection model for localizing lesions during DBE, and 3021 images were collected to construct a classification model for classifying lesions into four classes, protruding lesion, diverticulum, erosion & ulcer and angioectasia. The performance of the two models was evaluated using 1318 normal images and 915 abnormal images and 65 videos from independent patients and then compared with that of 8 endoscopists. The standard answer was the expert consensus. RESULTS: For the image test set, the detection model achieved a sensitivity of 92% (843/915) and an area under the curve (AUC) of 0.947, and the classification model achieved an accuracy of 86%. For the video test set, the accuracy of the system was significantly better than that of the endoscopists (85% vs. 77 +/- 6%, p < 0.01). For the video test set, the proposed system was superior to novices and comparable to experts. CONCLUSIONS: We established a real-time CAD system for detecting and classifying small bowel lesions in DBE with favourable performance. ENDOANGEL-DBE has the potential to help endoscopists, especially novices, in clinical practice and may reduce the miss rate of small bowel lesions. | Humans; Double-Balloon Enteroscopy/methods; *Deep Learning; Intestine, Small/diagnostic imaging/pathology; *Intestinal Diseases/diagnostic imaging; Abdomen/pathology; Endoscopy, Gastrointestinal/methods; Retrospective Studies | BMC gastroenterology | 2024 Jan 2 | 10.1186/s12876-023-03067-w [doi] 10 | https://pubmed.ncbi.nlm.nih.gov/38166722/ | not available | January 2024 |
38,717,342 | The case for bioanalytical analyzer. | Li M | null | *Chemistry Techniques, Analytical/methods | Bioanalysis | 2024 | 10.1080/17576180.2024.2344328 [doi] | https://pubmed.ncbi.nlm.nih.gov/38717342/ | not available | January 2024 |
38,303,446 | Quantifying the presymptomatic transmission of COVID-19 in the USA. | Zhang L; Zhang Z; Pei S; Gao Q; Chen W | The emergence of many presymptomatic hidden transmission events significantly complicated the intervention and control of the spread of COVID-19 in the USA during the year 2020. To analyze the role that presymptomatic infections play in the spread of this disease, we developed a state-level metapopulation model to simulate COVID-19 transmission in the USA in 2020 during which period the number of confirmed cases was more than in any other country. We estimated that the transmission rate (i.e., the number of new infections per unit time generated by an infected individual) of presymptomatic infections was approximately 59.9% the transmission rate of reported infections. We further estimated that at any point in time the average proportion of infected individuals in the presymptomatic stage was consistently over 50% of all infected individuals. Presymptomatic transmission was consistently contributing over 52% to daily new infections, as well as consistently contributing over 50% to the effective reproduction number from February to December. Finally, non-pharmaceutical intervention targeting presymptomatic infections was very effective in reducing the number of reported cases. These results reveal the significant contribution that presymptomatic transmission made to COVID-19 transmission in the USA during 2020, as well as pave the way for the design of effective disease control and mitigation strategies. | Humans; United States/epidemiology; *COVID-19/epidemiology; SARS-CoV-2; Asymptomatic Infections/epidemiology; Basic Reproduction Number | Mathematical biosciences and engineering : MBE | 2024 Jan | 10.3934/mbe.2024036 [doi] | https://pubmed.ncbi.nlm.nih.gov/38303446/ | not available | January 2024 |
38,291,364 | CCL-DTI: contributing the contrastive loss in drug-target interaction prediction. | Dehghan A; Abbasi K; Razzaghi P; Banadkuki H; Gharaghani S | BACKGROUND: The Drug-Target Interaction (DTI) prediction uses a drug molecule and a protein sequence as inputs to predict the binding affinity value. In recent years, deep learning-based models have gotten more attention. These methods have two modules: the feature extraction module and the task prediction module. In most deep learning-based approaches, a simple task prediction loss (i.e., categorical cross entropy for the classification task and mean squared error for the regression task) is used to learn the model. In machine learning, contrastive-based loss functions are developed to learn more discriminative feature space. In a deep learning-based model, extracting more discriminative feature space leads to performance improvement for the task prediction module. RESULTS: In this paper, we have used multimodal knowledge as input and proposed an attention-based fusion technique to combine this knowledge. Also, we investigate how utilizing contrastive loss function along the task prediction loss could help the approach to learn a more powerful model. Four contrastive loss functions are considered: (1) max-margin contrastive loss function, (2) triplet loss function, (3) Multi-class N-pair Loss Objective, and (4) NT-Xent loss function. The proposed model is evaluated using four well-known datasets: Wang et al. dataset, Luo's dataset, Davis, and KIBA datasets. CONCLUSIONS: Accordingly, after reviewing the state-of-the-art methods, we developed a multimodal feature extraction network by combining protein sequences and drug molecules, along with protein-protein interaction networks and drug-drug interaction networks. The results show it performs significantly better than the comparable state-of-the-art approaches. | Amino Acid Sequence; Drug Interactions; Entropy; *Knowledge; *Machine Learning | BMC bioinformatics | 2024 Jan 30 | 10.1186/s12859-024-05671-3 [doi] 48 | https://pubmed.ncbi.nlm.nih.gov/38291364/ | not available | January 2024 |
39,944,197 | Applications of Artificial Intelligence and Machine Learning in Emergency Medicine Triage - A Systematic Review. | Almulihi QA; Alquraini AA; Almulihi FAA; Alzahid AA; Al Qahtani SSAJ; Almulhim M; Alqhtani SHS; Alnafea FMN; Mushni SAS; Alaqil NA; Assiri MIF; Maghraby NH | BACKGROUND: Overcrowding in Emergency departments adversely impacts efficiency, patient outcomes, and resource allocation. Accurate triage systems are essential for prioritizing care and optimizing resources. While traditional methods provide a foundation, they often lack precision in addressing modern healthcare complexities. Artificial intelligence (AI) and machine learning (ML) offer advanced capabilities to enhance triage accuracy, improve patient prioritization, and support clinical decision-making, addressing limitations of conventional approaches and paving the way for adaptive triage solutions. OBJECTIVE: This systematic review aims to assess the use of artificial intelligence (AI) and machine learning (ML) in determining the outcomes of patients presenting in Emergency department (ED) triage. METHODS: A systematic search was conducted on April 21, 2023, using electronic databases including PubMed/Medline, Cochrane Library, Ovid, and Google Scholar, without year restrictions. The main outcome of this review was to assess the use of AI and ML in the ED Triage. Articles that used different models of AI and ML to predict various outcomes of patients in the ED setting were included. RESULTS: A total of 17 studies were included in this systematic review. Fifteen studies assessed the role of machine learning methods in emergency department triage, while two studies evaluated the role of AI and machine learning in prehospital triage. The results of our systematic review favor the use of machine learning methods and artificial intelligence in emergency triage. Machine learning models were found to be superior to conventional emergency severity score methods in determining triage, diagnosis, and early management of patients. Among the machine learning methods, the boosting model was slightly more effective. CONCLUSION: Our study supports the notion that AI and ML are the future of Emergency departments. They aid in predicting patient outcomes and determining appropriate management strategies more efficiently, thereby enhancing decision making in the ED. | *Triage/methods; Humans; *Machine Learning; *Artificial Intelligence; *Emergency Service, Hospital; Emergency Medicine/methods | Medical archives (Sarajevo, Bosnia and Herzegovina) | 2024 | 10.5455/medarh.2024.78.198-206 [doi] | https://pubmed.ncbi.nlm.nih.gov/39944197/ | not available | January 2024 |
38,314,912 | ChIP-GPT: a managed large language model for robust data extraction from biomedical database records. | Cinquin O | Increasing volumes of biomedical data are amassing in databases. Large-scale analyses of these data have wide-ranging applications in biology and medicine. Such analyses require tools to characterize and process entries at scale. However, existing tools, mainly centered on extracting predefined fields, often fail to comprehensively process database entries or correct evident errors-a task humans can easily perform. These tools also lack the ability to reason like domain experts, hindering their robustness and analytical depth. Recent advances with large language models (LLMs) provide a fundamentally new way to query databases. But while a tool such as ChatGPT is adept at answering questions about manually input records, challenges arise when scaling up this process. First, interactions with the LLM need to be automated. Second, limitations on input length may require a record pruning or summarization pre-processing step. Third, to behave reliably as desired, the LLM needs either well-designed, short, 'few-shot' examples, or fine-tuning based on a larger set of well-curated examples. Here, we report ChIP-GPT, based on fine-tuning of the generative pre-trained transformer (GPT) model Llama and on a program prompting the model iteratively and handling its generation of answer text. This model is designed to extract metadata from the Sequence Read Archive, emphasizing the identification of chromatin immunoprecipitation (ChIP) targets and cell lines. When trained with 100 examples, ChIP-GPT demonstrates 90-94% accuracy. Notably, it can seamlessly extract data from records with typos or absent field labels. Our proposed method is easily adaptable to customized questions and different databases. | Humans; *Medicine; Cell Line; Chromatin Immunoprecipitation; Databases, Factual; Language | Briefings in bioinformatics | 2024 Jan 22 | 10.1093/bib/bbad535 [doi] bbad535 | https://pubmed.ncbi.nlm.nih.gov/38314912/ | not available | January 2024 |
37,488,855 | Clinical associations of corneal neuromas with ocular surface diseases. | Toh CJL; Liu C; Lee IXY; Yu Lin MT; Tong L; Liu YC | Corneal neuromas, also termed microneuromas, refer to microscopic, irregularly-shaped enlargements of terminal subbasal nerve endings at sites of nerve damage or injury. The formation of corneal neuromas results from damage to corneal nerves, such as following corneal pathology or corneal or intraocular surgeries. Initially, denervated areas of sensory nerve fibers become invaded by sprouts of intact sensory nerve fibers, and later injured axons regenerate and new sprouts called neuromas develop. In recent years, analysis of corneal nerve abnormalities including corneal neuromas which can be identified using in vivo confocal microscopy, a non-invasive imaging technique with microscopic resolution, has been used to evaluate corneal neuropathy and ocular surface dysfunction. Corneal neuromas have been shown to be associated with clinical symptoms of discomfort and dryness of eyes, and are a promising surrogate biomarker for ocular surface diseases, such as neuropathic corneal pain, dry eye disease, diabetic corneal neuropathy, neurotrophic keratopathy, Sjogren's syndrome, bullous keratopathy, post-refractive surgery, and others. In this review, we have summarized the current literature on the association between these ocular surface diseases and the presentation of corneal microneuromas, as well as elaborated on their pathogenesis, visualization via in vivo confocal microscopy, and utility in monitoring treatment efficacy. As current quantitative analysis on neuromas mainly relies on manual annotation and quantification, which is user-dependent and labor-intensive, future direction includes the development of artificial intelligence software to identify and quantify these potential imaging biomarkers in a more automated and sensitive manner, allowing it to be applied in clinical settings more efficiently. Combining imaging and molecular biomarkers may also help elucidate the associations between corneal neuromas and ocular surface diseases. | null | Neural regeneration research | 2024 Jan | 10.4103/1673-5374.375308 [doi] | https://pubmed.ncbi.nlm.nih.gov/37488855/ | not available | January 2024 |
37,778,921 | Editorial: Do you believe artificial intelligence or my interpretation? | Maehara A | null | null | Cardiovascular revascularization medicine : including molecular interventions | 2024 Jan | S1553-8389(23)00830-8 [pii] 10.1016/j.carrev.2023.09.010 [doi] | https://pubmed.ncbi.nlm.nih.gov/37778921/ | not available | January 2024 |
39,625,215 | Enhancing coronary artery plaque analysis via artificial intelligence-driven cardiovascular computed tomography. | Xia J; Bachour K; Suleiman AM; Roberts JS; Sayed S; Cho GW | Coronary computed tomography angiography (CCTA) is a noninvasive imaging modality of cardiac structures and vasculature considered comparable to invasive coronary angiography for the evaluation of coronary artery disease (CAD) in several major cardiovascular guidelines. Conventional image acquisition, processing, and analysis of CCTA imaging have progressed significantly in the past decade through advances in technology, computation, and engineering. However, the advent of artificial intelligence (AI)-driven analysis of CCTA further drives past the limitations of conventional CCTA, allowing for greater achievements in speed, consistency, accuracy, and safety. AI-driven CCTA (AI-CCTA) has achieved a significant reduction in radiation exposure for patients, allowing for high-quality scans with sub-millisievert radiation doses. AI-CCTA has demonstrated comparable accuracy and consistency in manual coronary artery calcium scoring against expert human readers. An advantage over invasive coronary angiography, which provides luminal information only, CCTA allows for plaque characterization, providing detailed information on the quality of plaque and offering further prognosticative value for the management of CAD. Combined with AI, many recent studies demonstrate the efficacy, accuracy, efficiency, and precision of AI-driven analysis of CCTA imaging for the evaluation of CAD, including assessing degree stenosis, adverse plaque characteristics, and CT fractional flow reserve. The limitations of AI-CCTA include its early phase in investigation, the need for further improvements in AI modeling, possible medicolegal implications, and the need for further large-scale validation studies. Despite these limitations, AI-CCTA represents an important opportunity for improving cardiovascular care in an increasingly advanced and data-driven world of modern medicine. | Humans; *Coronary Artery Disease/diagnostic imaging/physiopathology; *Plaque, Atherosclerotic/diagnostic imaging; *Coronary Angiography; *Computed Tomography Angiography; *Predictive Value of Tests; *Artificial Intelligence; *Coronary Vessels/diagnostic imaging/physiopathology; *Radiographic Image Interpretation, Computer-Assisted; Prognosis; Reproducibility of Results; Severity of Illness Index | Therapeutic advances in cardiovascular disease | 2024 Jan-Dec | 10.1177/17539447241303399 [doi] 17539447241303399 | https://pubmed.ncbi.nlm.nih.gov/39625215/ | not available | January 2024 |
38,285,425 | Ethics: Crisis Standards of Care Simulation. | Fuller Switzer D; Knowles SG | Ethical dilemmas exist with decision-making regarding resource allocations, such as critical care, ventilators and other critical equipment, and pharmaceuticals during pandemics. Triage artificial intelligence (AI) algorithms based on prognostication tools exist to guide these decisions; however, implicit bias may affect the decision-making process leading to deviation from the algorithm recommendations. Conflict within the ethical domain may be affected as well. A knowledge gap was identified within the Adult-Gerontology Acute Care Nurse Practitioner (AG-ACNP) curriculum regarding ethics in crisis standards of care (CSC) medical decision-making. Incorporating a CSC simulation looked to address this knowledge gap. A simulation-based learning (SBL) experience was designed as a critical access setting where CSC are in place and three diverse, medically complex patients in need of critical care present to the hospital where one critical care bed remains open. Given the complexity of the simulation scenario, a table-top pilot test was selected. Three AG-ACNP fourth-quarter students in their critical care rotation volunteered for the pilot test. Students were provided with the topic, "ethics crisis standards of care" and the article, "A catalogue of tools and variables from crisis and routine care to support decision-making during pandemics" by M. Cardona et al. (2021), to read in advance. Students were provided with the triage AI algorithm (M. Cardona et al., 2021) utilizing prognostication tools to prioritize which patient requires the critical care bed. The expectation was that implicit bias would enter the decision-making process, causing deviation from the triage AI algorithm and moral distress. The debriefing session revealed that students deviated from the triage AI algorithm, experienced implicit bias, moral distress, and utilized clinical judgment and experience to care for all three patients. The pilot test results support that a CSC SBL experience addresses a critical knowledge gap in AG-ACNP education and an SBL experience incorporating ethical decision-making curriculum with standardized patients should be developed and trialed as the next step. | Adult; Humans; *Artificial Intelligence; *Standard of Care; Algorithms; Curriculum; Educational Status | Advanced emergency nursing journal | 2024 Jan-Mar 01 | 10.1097/TME.0000000000000498 [doi] | https://pubmed.ncbi.nlm.nih.gov/38285425/ | not available | January 2024 |
37,871,132 | Toward Explainable Artificial Intelligence for Precision Pathology. | Klauschen F; Dippel J; Keyl P; Jurmeister P; Bockmayr M; Mock A; Buchstab O; Alber M; Ruff L; Montavon G; Muller KR | The rapid development of precision medicine in recent years has started to challenge diagnostic pathology with respect to its ability to analyze histological images and increasingly large molecular profiling data in a quantitative, integrative, and standardized way. Artificial intelligence (AI) and, more precisely, deep learning technologies have recently demonstrated the potential to facilitate complex data analysis tasks, including clinical, histological, and molecular data for disease classification; tissue biomarker quantification; and clinical outcome prediction. This review provides a general introduction to AI and describes recent developments with a focus on applications in diagnostic pathology and beyond. We explain limitations including the black-box character of conventional AI and describe solutions to make machine learning decisions more transparent with so-called explainable AI. The purpose of the review is to foster a mutual understanding of both the biomedical and the AI side. To that end, in addition to providing an overview of the relevant foundations in pathology and machine learning, we present worked-through examples for a better practical understanding of what AI can achieve and how it should be done. | Humans; *Artificial Intelligence; *Precision Medicine | Annual review of pathology | 2024 Jan 24 | 10.1146/annurev-pathmechdis-051222-113147 [doi] | https://pubmed.ncbi.nlm.nih.gov/37871132/ | not available | January 2024 |
38,303,917 | Application and Potential of Artificial Intelligence in Heart Failure: Past, Present, and Future. | Yoon M; Park JJ; Hur T; Hua CH; Hussain M; Lee S; Choi DJ | The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications. | null | International journal of heart failure | 2024 Jan | 10.36628/ijhf.2023.0050 [doi] | https://pubmed.ncbi.nlm.nih.gov/38303917/ | not available | January 2024 |
37,713,220 | AIROGS: Artificial Intelligence for Robust Glaucoma Screening Challenge. | de Vente C; Vermeer KA; Jaccard N; Wang H; Sun H; Khader F; Truhn D; Aimyshev T; Zhanibekuly Y; Le TD; Galdran A; Ballester MAG; Carneiro G; Devika RG; Sethumadhavan HP; Puthussery D; Liu H; Yang Z; Kondo S; Kasai S; Wang E; Durvasula A; Heras J; Zapata MA; Araujo T; Aresta G; Bogunovic H; Arikan M; Lee YC; Cho HB; Choi YH; Qayyum A; Razzak I; van Ginneken B; Lemij HG; Sanchez CI | The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models for glaucoma screening from CFPs have shown promising results in laboratory settings, their performance decreases significantly in real-world scenarios due to the presence of out-of-distribution and low-quality images. To address this issue, we propose the Artificial Intelligence for Robust Glaucoma Screening (AIROGS) challenge. This challenge includes a large dataset of around 113,000 images from about 60,000 patients and 500 different screening centers, and encourages the development of algorithms that are robust to ungradable and unexpected input data. We evaluated solutions from 14 teams in this paper and found that the best teams performed similarly to a set of 20 expert ophthalmologists and optometrists. The highest-scoring team achieved an area under the receiver operating characteristic curve of 0.99 (95% CI: 0.98-0.99) for detecting ungradable images on-the-fly. Additionally, many of the algorithms showed robust performance when tested on three other publicly available datasets. These results demonstrate the feasibility of robust AI-enabled glaucoma screening. | Humans; *Artificial Intelligence; *Glaucoma/diagnostic imaging; Fundus Oculi; Diagnostic Techniques, Ophthalmological; Algorithms | IEEE transactions on medical imaging | 2024 Jan | 10.1109/TMI.2023.3313786 [doi] | https://pubmed.ncbi.nlm.nih.gov/37713220/ | not available | January 2024 |
37,813,700 | Artificial Intelligence-Driven Image Quality Selection During Myocardial Contrast Echocardiography: A New Path to Precision. | Cosyns B; Mulvagh SL | null | *Artificial Intelligence; *Echocardiography/methods; Myocardium | Ultrasound in medicine & biology | 2024 Jan | S0301-5629(23)00308-3 [pii] 10.1016/j.ultrasmedbio.2023.09.007 [doi] | https://pubmed.ncbi.nlm.nih.gov/37813700/ | not available | January 2024 |
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