COVID-19 SignSym: a fast adaptation of a general clinical NLP tool to identify and normalize COVID-19 signs and symptoms to OMOP common data model

Abstract The COVID-19 pandemic swept across the world rapidly, infecting millions of people. An efficient tool that can accurately recognize important clinical concepts of COVID-19 from free text in electronic health records (EHRs) will be valuable to accelerate COVID-19 clinical research. To this end, this study aims at adapting the existing CLAMP natural language processing tool to quickly build COVID-19 SignSym, which can extract COVID-19 signs/symptoms and their 8 attributes (body location, severity, temporal expression, subject, condition, uncertainty, negation, and course) from clinical text. The extracted information is also mapped to standard concepts in the Observational Medical Outcomes Partnership common data model. A hybrid approach of combining deep learning-based models, curated lexicons, and pattern-based rules was applied to quickly build the COVID-19 SignSym from CLAMP, with optimized performance. Our extensive evaluation using 3 external sites with clinical notes of COVID-19 patients, as well as the online medical dialogues of COVID-19, shows COVID-19 SignSym can achieve high performance across data sources. The workflow used for this study can be generalized to other use cases, where existing clinical natural language processing tools need to be customized for specific information needs within a short time. COVID-19 SignSym is freely accessible to the research community as a downloadable package (https://clamp.uth.edu/covid/nlp.php) and has been used by 16 healthcare organizations to support clinical research of COVID-19.

INTRODUCTION
The COVID-19 pandemic 1 swept across the world rapidly resulting in the death, as of February 23, 2021, of at least 500 104 people in the US. 2 Scientists and researchers from multiple organizations worldwide have been working collaboratively, targeting effective prevention and treatment strategies. 3 Research findings, data resources, informatics tools, and technologies are being openly shared, aiming to speed up the fight against such emerging pandemics. 4,5 Facilitated by the National Library of Medicine, large datasets of articles relevant to COVID-19 are being accumulated and shared at a rapid pace in the medical community. 6 For example, the COVID- 19 Open Research Dataset has already accumulated more than 75 000 full text articles. 6 Based on such resources, many tools have been developed using natural language processing (NLP) techniques to unlock COVID-19 information from the literature, including search engines, information extraction, and knowledge graph building. 7,8 As another important data source for COVID-19 research, electronic health records (EHRs) store the clinical data of COVID-19 patients, which are critical for various applications, such as clinical decision support, predictive modeling, and EHR-based clinical research. 9,10 An efficient tool that can accurately recognize important clinical concepts of COVID-19 from clinical notes will be highly valuable to save time and accelerate the chart-review workflow.
Despite that several large consortia have been formed to construct large clinical data networks for COVID-19 research, such as Open Health Data Science and Informatics (OHDSI), 11 the National COVID Cohort Collaborative (N3C), 12 and the international EHRderived COVID-19 Clinical Course Profiles, 13 limited tools have been developed for COVID-19 information extraction from clinical notes. Rich patient information, such as signs/symptoms together with their attributes, is recorded in clinical narratives, which is critical for COVID-19 surveillance, diagnosis and prognosis, and treatment effectiveness research. Therefore, natural language processing (NLP) techniques are needed to automatically extract important clinical information efficiently and accurately from clinical text. Clinical NLP communities, such as the OHDSI NLP working group and the N3C NLP working group, have quickly reacted and built tools such as COVID-19 TestNorm 14 for lab testing normalization and MedTagger COVID-19 pipeline. 15,16 Nevertheless, 1 common question worth answering is how to leverage existing general-purpose clinical NLP systems 17,18 to quickly respond to the urgent need of developing NLP pipelines for specific disease areas, such as COVID-19. Given that the datasets and annotations originally used to build the general-purpose clinical NLP systems are not dedicated to COVID-19 signs/symptoms, the learnt distributions of linguistic features and semantic types may not be sufficient to model the specific set of COVID-19 signs/symptoms, which leads to dropped performance for this specific task. To improve it, unique contextual patterns and lexicons may need to be created to tailor the existing clinical NLP system for COVID-19 information.
To address this question, in this study, we describe how to quickly build COVID-19 SignSym, which extracts COVID-19 signs/ symptoms and 8 attributes (body location, severity, temporal expression, subject, condition, uncertainty, negation, and course) by adapting existing components in the Clinical Language Annotation, Modeling, and Processing (CLAMP) NLP tool. 17 The extracted entities were also normalized to standard terms in the OHDSI Observational Medical Outcomes Partnership (OMOP) common data model (CDM) 19 automatically. The tool was originally developed and evaluated on the MIMIC-III dataset. It was then evaluated using clinical notes of COVID-19 patients from 3 external institutions, including UT Physicians (UTP), University of Kansas Medical Center (KUMC), and Johns Hopkins Hospital. In addition, we also examined and adapted it to social media data-online medical dialogues of COVID-19. Evaluations on clinical notes and medical dialogues demonstrate promising results. We believe this tool will provide great support to the secondary use of EHRs to accelerate clinical research of COVID-19. Figure 1 illustrates an overview of the workflow for building COVID-19 SignSym. This workflow mainly consisted of 5 steps: (1) Information model design to define the information scope of COVID-19 SignSym (ie, semantic types of clinical concepts and relations, to be extracted); (2) Sign/symptom collection from multiple sources to decide the scope of COVID-19 signs/symptoms; (3) Information extraction and normalization to extract and normalize COVID-19 signs/symptoms and 8 types of attributes, including body location, severity, temporal expression, subject, condition, uncertainty, negation, and course. Hybrid approaches were used to adapt existing CLAMP NLP pipelines for COVID-19 information extraction. Lexicons of COVID-19 signs/symptoms and patternbased rules were integrated with CLAMP to optimize the performance. All signs/symptoms in free text were extracted first, then filtered by lexicons and Unified Medical Language System (UMLS) concept unique identifiers (CUIs) to obtain COVID-19-specific signs/symptoms. (4) OMOP mapping to convert the output information into the OMOP CDM concepts.

Datasets
Five clinical datasets were used for building and evaluating the pipeline; these included MIMIC-III (used for pipeline construction and internal validation) and clinical notes data from UTP, KUMC, and Johns Hopkins, as well as Medical Dialogs. The external clinical datasets were annotated independently at each site (ie, UTP, KUMC, and Johns Hopkins). Cohen's Kappa score was reported for interannotator agreement of each dataset between at least 2 annotators.  20 Notes were preprocessed by Hopkins in-house section identification tools, and only the relevant narrative parts, particularly the chief complaint and history of the present illness sections, were extracted. For each of the 40 patients, each symptom was labeled as present or not present, resulting in over 467 manually annotated symptoms. These gold standard labels were then used to validate COVID-19 SignSym. The reported interannotator agreement was 0.970. In total, the clinical notes in this dataset contained 13 397 sentences and 121 802 tokens. 5. Medical dialogues: This dataset contained medical dialogues related to COVID-19 collected from an online website dialog between patients and doctors. 21 Initially, 50 dialogues were randomly selected for external evaluation. However, a sharp performance drop was observed on several attributes, such as temporal information, due to unique patterns in medical dialogues. Therefore, these 50 dialogues were used for tuning the pipeline and another 50 dialogues were used for an open test of COVID-19 SignSym. The reported interannotator agreement was 0.914. In total, the selected dataset contained 1162 sentences and 22 324 tokens.
The statistics of data sources used for evaluation in this study are summarized in Table 1.

Information model
The information model followed by the COVID-19 SignSym is illustrated in Figure 2. In addition to mentions of signs and symptoms, 8 important attributes and their relations were recognized 22 :

Lexicon building for COVID-19 signs and symptoms
Leveraging the community efforts, COVID-19 signs and symptoms were collected from 5 sources: (1) World Health Organization (WHO) case record form: 26 signs and symptoms were collected from the SIGNs AND SYMPTOMS ON ADMISSION section of the case record form provided by WHO. 23 (2) N3C: 15 signs and symptoms were collected from the diagnosis table shared by the N3C as phenotyping information. 12 (3) MedTagger Lexicon: 17 signs and symptoms together with their 136 synonyms were collected from the lexicon in MedTagger. 16 (4) Lexicon from Johns Hopkins: 14 signs and symptoms, and 337 synonyms were collected from an in-house lexicon of Johns Hopkins. (5) UMLS CUIs of these signs and symptoms were also assigned manually, with their UMLS synonyms collected. After removing redundancy, 55 signs and symptoms (with 153 different CUIs in total) and 3213 synonyms were collected from these sources. Notably, the reason why 55 signs and symptoms have 153 UMLS CUIs is that some signs/symptoms are represented by multiple semantic concepts with finer-granularities in UMLS. For example, productive cough is represented as productive cough-green sputum (CUI: C0425504), productive cough-clear sputum (CUI: C0425505), and productive cough-yellow sputum (CUI: C0425506) in UMLS. Table 2 illustrates 10 signs/symptoms of COVID-19 together with their CUIs and example synonyms. A comprehensive list of COVID-19 signs, symptoms, and their corresponding UMLS CUIs and synonyms can be accessed through GitHub. 24 Building pipelines of information extraction and normalization using CLAMP Problem-attributes pipeline in CLAMP: The problem-attribute pipeline in CLAMP 18 was adapted for COVID-19 information extraction in this study. This pipeline was built to automatically extract mentions of problems and their 8 attributes from clinical text. The definition of problems follows that used in the i2b2 2010 shared task, which consisted of 11 semantic types in UMLS (eg, sign or symptom, pathologic functions, disease or syndrome, etc). 25 The definitions of attributes follow those used in the SemEval 2015 shared task 14. 22 The pipeline used a hybrid approach to recognize mentions of problems and their attributes by combining the deep learning-based method, domain lexicons, and rules. Both the recognition of problems and attributes were represented as a sequential labeling task. 26 In particular, a novel joint-learning framework was designed to recognize attributes and their relations with problems simultaneously by integrating problem mentions as features into the deep learning architecture. 27 To balance the time efficiency and performance in a practical clinical NLP tool 28 , the deep learning-based Bi-directional Long Short-Term Memory Conditional Random Fields (BiLSTM-CRF) algorithm 26 was used to recognize mentions of clinical concepts, with 200-dimension word embeddings of MIMIC-III as the initial input.
Dictionary lookup for COVID-19 signs and symptoms: The lexicons previously collected were used in an additional step of dictionary-lookup to improve the coverage of recognized COVID-19 signs and symptoms. Regular expressions and rules were also applied in a postprocessing step to boost the performance of attribute recognition.
Concept normalization: Mentions of problems and attributes were normalized to standard concepts using the UMLS encoder module of CLAMP, which were built on a hybrid method of semantic similarity-based ranking and rules of concept prevalence in UMLS. 29 Both CUIs and preferred terms in UMLS were output for each recognized entity.
Filtering of COVID-19 signs/symptoms: Once medical problems were automatically recognized and normalized to UMLS CUIs, they were filtered by the precollected lexicons and CUIs of COVID-19 signs and symptoms.
OMOP concept mapping: The remaining signs/symptoms and their attributes were also mapped to standard concepts in the OMOP CDM. The OMOP encoder module of CLAMP was used for this purpose, which applied a similar approach as in the UMLS encoder module with a different scope of standard concepts and identifiers. Finally, COVID-19 SignSym outputs tab-delaminated files of normalized clinical concepts, with a format ready for importing into the Note_NLP table of OMOP CDM. 30 Evaluation Evaluation of information extraction: (1) Internal evaluation on MIMIC-III: As mentioned above, COVID-19 SignSym is a pipeline For UTP notes: (1) False positive signs and symptoms within wrong context: since a dictionary lookup method using sign/symptom lexicons of COVID-19 is carried out to improve the recall, some lexicons are matched in a wrong context, such as flu in "flu shot" and influenza in "Influenza vaccine." Semantic types in UMLS were used to filter out such false positives. (2) Semistructure list: some attributes, such as conditions, are modifying signs/symptoms in a list of multiple items. Regular expressions were designed to parse common semistructure patterns.
For medical dialogues: (3) New temporal patterns: some temporal expressions have new patterns and contexts uncovered in previous training datasets, such as in "Saturday February 29thdiarrhea." To address this problem, new temporal terms and pattern-based rules were added to the pipeline.
For both UTP notes and medical dialogues: (4) New synonyms of signs/symptoms: for example, "breathing harder," "cannot breathe" (original typos are kept here) are mentions of "shortness of breath." Another error is caused by partial recognition of named entities: For example, "cough" is recognized instead of "cough productive of yellow sputum." Unseen mentions were used to enrich the lexicon to improve its coverage. (5) Question marks as false positive uncertainty: both clinical text and medical dialogues contain questions about patients' signs and symptoms. According to our current annotation guideline, if the question has answers along with it, then the question mark will not be considered as a mention of uncertainty; or else, it will be labeled as an uncertainty attribute. (6) New patterns of subjects: many subjects in medical dialogues are family members of the question asker, different from the common cases in clinical text, such as in "my daughter is 3 and a half. . . has had pneumonia." Besides, false positives also occur when the family member's statement is cited in the clinical text, such as in "Per his wife he had an episode of confusion." Since the subject attribute is of low frequency in the text, it is hard to capture the complex context of it. Syntactic structures will be applied in the next step in attempt to resolve this problem.
Evaluation of concept normalization: 1000 terms were randomly selected from the UTP notes and manually reviewed to evaluate the performance of clinical concept normalization.
Use case-based evaluation: A use case of identifying patients' signs/symptoms at presentation from notes generated within 72/48 hours before and after admission at KUMC/Johns Hopkins Hospital was used to validate the effectiveness of COVID-19 SignSym. Specifically, the task was to recognize signs and symptoms of the patient, in order to assist in future COVID-19 diagnosis and research. Specifically, once signs and symptoms were recognized in clinical text and normalized, they were also determined as negated, possible, or positive using attributes in their context. Only positive signs/symptoms were kept, and they were aggregated into the patient level to compare with the gold standard annotations of signs and symptoms manually recorded for each patient.
Evaluation criteria: (1) The end-to-end performances of extracting signs, symptoms, and their attributes were evaluated using precision, recall, and F-measure (F1) by exact match between manual and automatic annotations. (2) The performance of concept normalization was evaluated using accuracy. (3) The performances of patient-level extraction of COVID-19 signs/symptoms were evaluated using precision, recall, and F-measure.

RESULTS
Information extraction: Internal evaluation performances for COVID-19 information extraction on MIMIC-III are reported in Table 3. The F-measure of signs/symptoms was enhanced from 0.846 to 0.972 after improving the original CLAMP pipeline for COVID-19 information. Especially, the recall was increased by 27.02%-from 0.781 to 0.992. Given that a joint learning approach was applied for recognizing the mentions of attributes and their relations with signs/symptoms, the performance enhancement on signs/ symptoms was also propagated to the attribute recognition. The recalls of attributes also increased sharply. For example, the recall of body location was improved from 0.551 to 0.965, leading to an improvement of the F-measure from 0.688 to 0.954. For attributes with low frequencies, their performances are relatively modest. For example, severity has only 24 instances in the test dataset, with an F-measure of 0.857.
External evaluation performances on clinical notes of UTP and the medical dialogue are illustrated in Table 4. The performances of the original COVID-19 SignSym pipeline and after adaptation are reported. As shown in Table 3, performances of COVID-19 Sign- Sym are improved after adapting the pipeline to external data, especially for the 3 attributes-temporal expression, condition, and uncertainty. Notably, promising results were achieved on sign/ symptom extraction, with F-measures of 0.986 on UTP notes and 0.99 on the medical dialogues. As for recognizing attributes of signs/ symptoms, the tool yielded better performances on attributes with higher frequencies (eg, F-measure: Body location, 0.992 for UTP notes and 0.94 for medical dialogue; Negation, 0.999 for UTP notes and 1.00 for medical dialogue). Similar to the internal evaluation, some semantic types have low frequencies in the test datasets with modest performances. Use case-based evaluation: (1) COVID-19 sign/symptom presentation of patients at KUMC: In comparison with manually assigned signs/symptoms of each patient, the tool yielded a precision of 0.908, a recall of 0.917, and an F-measure of 0.909. (2) COVID-19 sign/symptom presentation of patients at Johns Hopkins Hospital: In comparison with manually assigned signs/symptoms of each patient, the tool yielded a precision of 0.928, a recall of 0.957, and an F-measure of 0.942.

Tool availability
This tool is freely accessible to the research community as a downloadable package (https://clamp.uth.edu/covid/nlp.php). 18 A visualization of the pipeline output is illustrated in Figure 3.

DISCUSSION
There is an urgent demand for automated tools that can extract COVID-19 information from clinical text. This article presents our attempt to quickly adapt an existing general clinical NLP tool for COVID-19 sign/symptom extraction and normalization, leveraging COVID-19 lexicons provided from multiple resources. The first version of the COVID-19 SignSym was released on May 10, 2020, probably 1 of the earliest NLP tools for COVID-19 information extraction from clinical notes. Since then, over 16 healthcare organizations have used the tool to support diverse applications including those in KU and Johns Hopkins Hospital, indicating the utility of such a tool in the pandemic.
Several interesting findings have been observed, which may be generalized to other use cases, where existing clinical NLP tools need to be quickly customized for specific information needs. At the beginning of the pandemic, little COVID-19 clinical data (eg, notes) were available for developing data-driven approaches (eg, machine learning-based ones). Therefore, rule-based approaches that leverage knowledge from domain experts or existing knowledge bases could be built quickly and our results also show that such methods could achieve good performance on sign and symptom recognition. Moreover, our results also show that previously existing clinical corpora of similar diseases (eg, signs and symptoms of influenza in MIMIC-III) could be useful for developing methods for the new disease. Additionally, the deep learning-based disease-modifier models in the CLAMP tool also demonstrated reasonable performance when they were applied to COVID-19 signs and symptoms, indicating the adaption process could be mainly focused on defining postprocessing rules (instead of reannotating text and retraining models), thus saving the development time for the new NLP pipelines.
Nevertheless, the developed COVID-19 SignSym pipeline did not always perform well when applied to different sites. For example, it worked well on KU and Johns Hopkins corpora; but direct application to UTP notes showed a drop in performance and required local customization, especially for certain types of entities and probably due to the intrinsic syntactic and semantic variance of clinical notes from different sites. As indicated from the error analysis, attributes with low frequencies in medical text suffer from a relatively sharper performance drop when applying the pipeline to a new dataset, probably because of new patterns uncovered in the previous training data. Also, the low frequencies make their performances vulnerable. For example, if an attribute has only 20 occurrences in the dataset, 1 false positive will yield a 5% drop in precision. Despite using pretrained language models of clinical text and deep learning-based methods for model building, there is still space for improvement, such as increasing the scale and coverage of the unlabeled clinical text used to build the pretrained language model and automatically adapting the current pipelines to new datasets.
It would be interesting to investigate the lexicon variations of COVID-19 signs/symptoms among multiple sites. After removing lexical redundancies, the overlap among signs/symptoms annotated on MIMIC-III, UTP notes, and medical dialogues are illustrated in the Venn diagram of Figure 4. Only 31 mentions of signs/symptoms (eg, abdominal pain, cough, chest pain) are present in all 3 data Limitations and future work: this work has several limitations and future work is needed in several areas. While performance on 5 COVID-19 datasets is evaluated and reported, additional evaluations are needed to further refine the tool and increase its generaliz-  ability. At this stage, lexicon and pattern-based rules are used for a fast adaptation of the pipeline to new datasets, based on detailed error analysis. This will serve as a foundation for later automatic adaptation such as semisupervised learning and automatic pattern detection when external datasets of larger sizes are available. Note that extracted phenotyping information representations will be applied in downstream clinical applications in our collaborator's site. Currently, only sign/symptoms and their attributes are extracted; additional work will be conducted for more related information such as comorbidities and medications. Finally, the output information will also be mapped to other clinical data standards, such as FHIR, in the near future to facilitate clinical operations and other applications.

CONCLUSION
This article presents an automatic tool, COVID-19 SignSym, to extract and normalize signs/symptoms and their 8 attributes from clinical text by adapting a general clinical NLP tool (CLAMP) to COVID-19 information. COVID-19 SignSym achieved great performance in external evaluation using clinical notes of COVID-19 patients from 3 institutions and a social media dataset of medical dialogues and has been used to facilitate COVID-19 research. The workflow used for this study may be generalized to other use cases, where existing clinical NLP tools need to be customized for specific information needs within a short time.

FUNDING
This study is partially supported by the National Center for Advancing Translational Sciences grant number R44TR003254 and CTSA grant number UL1TR002366.

AUTHOR CONTRIBUTIONS
The work presented here was carried out in collaboration among all authors. YZ, MR, and HX designed methods and experiments. NA, MR, JG, HP, YZ, XS, ML, and JW annotated the datasets. HP, JW, NA, and MR analyzed the data and interpreted the results. YZ and FM drafted the article. All authors have contributed to, edited, reviewed, and approved the manuscript.

DATA AVAILABILITY STATEMENT
Annotation datasets of MIMIC-III and medical dialogues are available on request.

SUPPLEMENTARY MATERIAL
Supplementary material is available at Journal of the American Medical Informatics Association online.


extfieldsofstudy:
Computer Science
Engineering

text:
Hepatocellular Carcinoma Segmentation from Digital Subtraction Angiography Videos using Learnable Temporal Difference

Automatic segmentation of hepatocellular carcinoma (HCC) in Digital Subtraction Angiography (DSA) videos can assist radiologists in efficient diagnosis of HCC and accurate evaluation of tumors in clinical practice. Few studies have investigated HCC segmentation from DSA videos. It shows great challenging due to motion artifacts in filming, ambiguous boundaries of tumor regions and high similarity in imaging to other anatomical tissues. In this paper, we raise the problem of HCC segmentation in DSA videos, and build our own DSA dataset. We also propose a novel segmentation network called DSA-LTDNet, including a segmentation sub-network, a temporal difference learning (TDL) module and a liver region segmentation (LRS) sub-network for providing additional guidance. DSA-LTDNet is preferable for learning the latent motion information from DSA videos proactively and boosting segmentation performance. All of experiments are conducted on our self-collected dataset. Experimental results show that DSA-LTDNet increases the DICE score by nearly 4% compared to the U-Net baseline.

Introduction
Digital Subtraction Angiography (DSA) is good at displaying vascular lesions, thus it is widely used in clinical detection of blood flow, stenosis, thrombosis, etc. DSA has been one of the mainstream medical imaging tools for evaluating blood supply of tumors in Transcatheter Arterial Chemoembolization (TACE) for Hepatocellular Carcinoma (HCC) [1]. It is also essential for quantitative analysis of tumor blood supply in clinical diagnosis, which provides important information and guidance for operative drug planning and postoperative prognostic. In particular, radiologists are usually required to accurately identify multiple tumors of various sizes, healthy anatomical tissues and motion artifacts before evaluating tumors when the imaging of tumors becomes stable in videos. Automatically segmenting HCC from DSA videos can assist radiologists in locating and delineating tumor regions quickly, which is of great value to clinical diagnosis.
It is regretful that few studies have investigated segmentation of DSA videos before. Segmentation of DSA Videos is similar to the video object segmentation (VOS) task. There are several methods of the VOS task using traditional object detection solutions, such as optical flow, frame difference and background subtraction [2,3,4]. These solutions focus on learning objects' displacement to improve object detection performance. Apparent temporal motion information of tumors also exists in DSA videos. However, different from the VOS task, the HCC segmentation aims to segment tumors on one key frame ( Fig.1) without inputting segmentation masks. The temporal motion information of tumors in DSA video reflects in regional expansion, color tarnish and edge sharpness over time, which is distinct from the information in the VOS task of natural scenes as well. Thus, the methods of VOS cannot be used directly in our task.
Although the HCC segmentation can be solved with classic methods of medical image segmentation, like U-Net [5], the results based on these methods are not satisfactory (section 4.3). Two key issues in our problem remain unsolved: (1) Inevitable and irregular motion artifacts appearing throughout DSA videos especially those near the liver regions, slash the contrast of tumors and blur the boundary of tumors, which impairs the segmentation performance greatly.
(2) Other anatomical structures (like gastrointestinal tissue filled with contrast agent) have similar shape, contrast and intensity to tumors in DSA videos leading to segmentation inaccuracy. Therefore, precise and robust segmentation is highly desired and difficult for DSA videos at the same time.
In this paper, we propose a novel segmentation network called DSA-LTDNet to address the aforementioned issues by learning the distinct motion information of tumors from DSA videos and integrating the prior anatomical knowledge of liver regions as a supplemental information. All experiments are performed on our self-collected dataset. The experimental results of our method are improved by nearly 4% of DICE coefficient compared to the baseline. Our main contributions are: 1) We raise the problem of HCC segmentation in DSA videos for the first time, which is significant and challenging in clinical practice. We also build our selfcollected DSA dataset of 488 samples, with careful annotations; 2) The creative proposed temporal difference learning network can efficiently capture the distinct motion information of tumors from DSA videos, which is helpful for delineating tumor regions; 3) Prior anatomical knowledge of liver regions integrated in our network can support locating tumor regions.

Methodology
The pipeline of the proposed DSA-LTDNet is plotted in Fig.2, including a temporal difference learning (TDL) module, a liver region segmentation (LRS) subnetwork and a final fusion segmentation (FFS) sub-network. We also develop a simple approach for selecting key frames. The proposed TDL with multi-frame inputs is adopted for auto-learning temporal difference under the supervision of frame difference images. Our method also integrates prior anatomical knowledge of liver regions as guidance. The 3 inputs of FFS are the key frame, liver region masks predicted by LRS and the learned temporal difference by TDL.

Key Frame Selection and Segmentation
Selection In clinical practice, radiologists choose the frame where the imaging of tumors is the clearest, the most stable and the most complete in the DSA video, for diagnosis and measurement of tumor parameters. Thus, we define such frames as the key frames and pick out them from DSA videos as a simulation of diagnosis process. We design a simple method of automatic selection for this task. Based on an observed regular pattern, we firstly calculate the difference image between 2 adjacent frames in the last 15 frames of DSA videos and sum to gain the total pixel values. To judge whether a frame is the key frame, we average the pixel values of the 2 difference images before and after the frame. We select the frame with the minimum average pixel value as the key frame Segmentation Several classical medical image segmentation networks using the single key frame as the input are tested. Their segmentation results on the key frames (section 4.2.) are similar to each other. Their overall performances are barely satisfactory due to the challenging work. We finally choose the basic U-Net as the baseline because it is the easiest one to implement and modify.

Temporal Difference Learning
Motion Information The result of the U-Net baseline on the key frames reflect many problems, including multiple false positive and negative regions in the segmentation mask (section 4.3). However, the motion information in the frames, such as the pixel-based frame difference (Fig.1), can help delineate the tumor regions better. Consequently, we propose to add motion information across frames into our network for superior segmentation performance. We attempted 3 classic VOS methods of extracting motion information, which are the optical flow [6], frame difference [7] and background subtraction difference [8]. Our experiments (section 4.2 and Table 2) show that FSS with inputs, key frames(KF) concating frame differences(FD) performs best(71.73%). FDs are calculated between the (k − 9) − th and the k − th for the purpose of acquiring the most representative difference from the k − th key frame (Fig.2). We choose the parameter, 9, based on our experimental results with different parameters: 5, 7, 9, 11, 13. The model with k-9 has the best result due to its bigger difference than that of 5, 7 and less noise than that of 11, 13 according to our analysis.
Learnable Difference Such motion information as FD is simple and direct, while humans can obtain more complex motion information. Thus, we also propose a Temporal Difference Learning (TDL) module, to better learn the latent temporal difference automatically. TDL module concatenates the 10 consecutive frames (Fig.2) as its input and uses U-Net to learn the latent motion information from the input. We believe that this module has the ability to dig out more useful motion information than using the frame difference directly. However, the results in section 4.2 show that TDL cannot perform well without any supervision because it is difficult to converge. Therefore, we creatively use the frame difference as ancillary information to supervise TDL. Specifically, we creatively initialize TDL guided by FD, and finetune it with a relatively small loss in DSA-LTDNet. Experimental results (section 4.2) show that the learned temporal difference of TDL can effectively boost the segmentation performance quantitatively and qualitatively.

Liver Region Guidance
Our experimental results show that the basic models on the key frame sometimes segment the tumor mask outside the liver region (Fig.3), while such mistakes do not occur in clinical practice due to their medical knowledge that tumors are always located in or near the liver regions which generally appear at the top left of DSA videos (Fig.1). Such mistakes happen because some anatomical structures (especially gastrointestinal tissues and healthy liver tissues filled with contrast agent) have highly similar shape, contrast and intensity to tumors in DSA videos. Consequently, the LRS sub-network based on U-Net, is co-trained on the key frames to predict the liver regions. The predicted liver region masks (LM) produced by LRS are plugged the spatial prior knowledge of tumor regions into our network, which successfully guides our model to locate tumors accurately.

Architecture and Loss Function
We integrate the aforementioned module and sub-network into the whole architecture (Fig.2). Key frames (256 × 256 × 1), along with temporal difference (256 x 256 x 1) learned by LTD and the predicted liver region masks (256 × 256 × 1) by LRS, are concatenated and fed into the final fusion segmentation(FFS) network. We co-train the 3 networks simultaneously. The followings are the loss functions: Where L LT D denotes the loss of LTD; L LRS denotes the loss of LRS; L seg denotes the segmentation loss of tumors; L T otal is the total loss of the whole network ); a, λ 0 and λ 1 are 0.5, 0.1 and 1 respectively.

Dataset Construction
Collection The DSA data is acquired from 486 HCC patients in the TACE procedure (3 DSA images are acquired form one patient, giving 488 samples in total) with 362 for training and 124 for testing at Hospital A. All of patients have been diagnosed with HCC in BCLC stage B [9] among January 2010 and December 2019, which was confirmed by biopsy or radiological imaging studies according to the guidelines [10]. The included criteria are as followings: 1) over 18 years of age; 2) without any prior treatment or surgery for HCC and any known active malignancy in the past 3 years; 3) Child-Pugh class A or B [11]; 4) Eastern Cooperative Oncology Group (ECOG) [12] performance scores are 2 or below. The following situations are excluded in the selection: 1) concurrent ischemic heart disease or heart failure; 2) history of acute tumor rupture with hemoperitoneum; 3) Child-Pugh class C cirrhosis; 4) history of hepatic encephalopathy, intractable ascites, biliary obstruction, and variceal bleeding; 5) extrahepatic metastasis, and tumor invasion of the portal vein or branch.
Annotation Each DSA video contains 20-30 frames with 1021×788 pixel-wise resolution. We invited experienced radiologists to annotate the ground truth of the HCC and the corresponding liver regions. We augment the data by selecting 2 more consecutive frames after the key frame for the training, which is similar to key frame because the imaging of tumors keeps stable shortly after the key frame. All of annotations are performed with Labelme platform [13]. There are 760 annotated tumors in total with 212 tumors of poor blood, and 548 tumors of rich blood in our DSA dataset of 488 samples. Note that each patient has up to 10 tumors. The range of tumor size is [6.5, 217] and the average is 69.37 in pixels.

Implementation Details
All the models are trained from scratch based on PyTorch, using a NVIDIA GTX 8119MiB GPU. We do not apply the popular data augmentation (e.g., rotation) due to the special anatomical knowledge of HCC and liver area. In the training settings, the batch size is set to 8 and our proposed network consisted of 3 models is co-trained for 150 epochs. We used the Adam optimizer and the CosineAnnealingLR scheduler to update the network parameters. The initial learning rate is set to 0.001. In experimenting U-Net, Attention U-Net [14] U-Net++ [15] and nnU-Net [16] are trained separately to segment tumors on the key frame at first. At the second stage, we concat the key frame, with one of the followings respectively, which are optical flow, frame difference, background subtraction, learned temporal difference and predicted liver region masks and trained the segmentation networks for comparison. Eventually, we combine the TDL module and the LRS sub-network with the FFS sub-network, and cotrained the DSA-LTDNet.

Quantitative Analysis
The results of basic models and our proposed networks are in Table 1. The left part of Table 1 displays that 4 basic models, U-Net, Attention U-Net U-Net++ and nnU-Net have similar segmentation capabilities on the key frames. That tumors of poor blood supply have low contrast and blurry boundaries in DSA videos, along with other mentioned problems, brings great difficulty to HCC segmentation of basic models. Considering models' complexity of implementation and modification, we choose the easiest U-Net as the backbone of DSA-LTDNet. U-Net's dice score is 70.75%, which is regarded as our baseline. In the right part, it is worth noting that our proposed auxiliary models substantially improve the performance of HCC segmentation in DSA videos. With LRS and TDL added separately, the results are improved by 1.26% and 1.57%. Note that, the proposed method with both TDL and LRS added, achieves the best result, which is higher than U-Net by 2.97%. These numbers indicate that our methods take remarkable effects. The combination of LRS and TDL is successful as well.
At the second stage of our experiments, we tried to use traditional object detection methods as auxiliary information to support the network learn the motion information from DSA videos and segment tumors. The results of experiments in this stage are listed in Table 2. The traditional methods did not work well. The results of optical flow and background subtraction are even reduced by 2.40% and 1.9%. It is because that the motion information of other anatomical tissues displays similarly in optical flow and background subtraction as well, which increases the difficulty of final segmentation instead. Afterwards, we used a CNN network to automatically learn the latent temporal motion without any supervision but it did not succeed as expected, whose result is 69.90%. According to our analysis, the failure is caused by the directionless learning without supervision, which might confuse the TDL network and feedback some noise information to the FFS network. We ceatively propose to use frame difference, which increases the result by 0.98%, to guide the TDL network. Eventually, this architecture improves the results by 1.57% compared to the baseline, which satisfies our expectations.

Qualitative Results
Baseline+LRS DSA-LTDNet 489,719,690, 551,  Fig. 3 illustrates a visual comparison of the ground truth, 2 basic models, baseline with TDL, baseline with LRS and our DSA-LTDNet on the key frame. It displays that basic models successfully produce accurate segmentation on tumors, but tumors' locations and shapes are not as accurate as our proposed methods. Such observations are particularly apparent in the first 3 rows of Fig.  3. Moreover, our proposed methods can obtain more accurate results by removing false positive regions under the guidance of liver regions and reducing the negative effect of motion artifacts, compared with basic models in the first 3 rows. It is noteworthy that DSA-LTDNet's performance on multiple small tumors marked blue in the last row, is significantly better than that of basic models due to the effective learning of temporal difference.

Discussions and Conclusions
We raise the problem of the HCC segmentation in DSA videos, which is proven significant and challenging in clinical practice. The novel DSA-LTDNet with a dominant TDL module, a supplementary LRS sub-network and a FFS subnetowrk is proposed. The TDL module can efficiently capture the distinct motion information of changing tumors for delineating tumor regions, while the prior anatomical knowledge of liver regions produced by LRS can support locating tumor regions. We also collect and annotate a relatively large DSA dataset, where we tested our networks. Experimental results show that our networks can effectively learn the latent motion information and achieve the state-of-the-art performance (DICE coefficient: 0.7368) on commonly used evaluation metrics. In the future, in order to improve the robustness, we plan to apply our networks to other DSA datasets from multiple hospitals, which are being collected. We will also facilitate the translation of CNN to clinical practice, especially in the treatment of HCC in the future.


Explosion-related deaths: An overview on forensic evaluation and implications

Purpose Explosion-related deaths are uncommon events which require expertise and confidence so that an appropriate death investigation can be performed. The present study aims to provide a detailed forensic analysis of the issues and implications arising in the event of an explosion. Methods A retrospective review of casualty data was conducted on electronic literature databases. Cases concerning deadly explosions registered at the Milan Institute of Legal Medicine were examined and analyzed altogether. Results Explosions may involve closed or open systems. A security assessment of the site is always necessary. Alterations of the site due to rescue procedures can occur; thus, on-site forensic investigation should be adapted to the environment. Then, a study protocol based on autopsy procedures is presented. Application of the postmortem radiology enforces forensic procedures both for the analysis of blast injuries and skeleton fractures, and for identification purposes. Blast injuries typically cause lacerations of the lungs, intestine and major vessels; moreover, hyoid fractures can be documented. Histopathology may help to define blast injuries effectively. Forensic chemistry, toxicology and ballistics provide useful investigative evidence as well as anthropology and genetics. Different forensic topics regarding explosions are discussed through five possible scenarios that forensic pathologists may come across. Scenarios include self-inflicted explosion deaths, domestic explosions, work-related explosions, terrorist events, and explosions caused by accidents involving heavy vehicles. Conclusion The scenarios presented offer a useful instrument to avoid misinterpretations and evaluation errors. Procedural notes and technical aspects are provided to the readers, with an insight on collaboration with other forensic experts.

Introduction
An explosion is defined as a violent and sudden fluid expansion, which determines a huge and rapid rise of pressure in the existing space (air or water) [1]. The main disruptive effects are caused by the movement of great air (or water) masses which provoke a rapid succession of compressive and decompressive waves [2]. Explosions can be triggered by airplane crashes, domestic gas leaks, fireworks and bombs [3,4]. Chemical devices (bombs) are divided into low-order and high-order explosives on the basis of the speed of detonation: the first ones include black powder and smokeless powder, while the second ones are mainly represented by dynamite, cyclonite, trinitrotoluene, cyclotrimethylene trinitramine, ammonium nitrate-fuel oil and plastic explosives (e.g. C4, PE4, Semtex) [1,[5][6][7].
Factors such as the amount and composition of explosive material, environment, delivery methods or the distance between the victim and the explosive device are all important elements in defining the pattern, as well as the extent of injuries caused by an explosion [2,8]. Barotrauma damages configure the major mechanism of wounding and mortality, as the shock waves, coming into contact with the gases of the lungs and the viscera, cause fatal blast injuries [3,9]. Furthermore, blast waves can lead to the collapse of buildings and the combination of thermal energy and toxic substances released during the deflagration can result in a widespread multitude of very challenging scenarios for the forensic pathologist [1,4,10]. The pathophysiology of explosion-related deaths is complicated and subordinated to many factors: direct blast injuries, injuries generated from the perforation of the body by large fragments during the explosion, traumatic asphyxia in the event of a building collapsing, and acute distress respiratory syndrome due to chemical pneumonia and thermal injury [1,7,9,[11][12][13]. Injury profiles are unique if compared to non-blast accidents and as a consequence they have been categorized as four distinct groups, listed in Table 1.
Many elements must be considered by forensic pathologists in the event of an explosion in order to perform a proper medicolegal death investigation. Among them, on-site investigation is essential due to the diversity of environments where an explosion-related death can occur [14][15][16]. Blasts are associated with severe damage. In particular, a variety of skin injuries can be observed at the autopsy: abrasions, bruises, lacerations, various degrees of burns, including charring, damage to the extremities or body disruption. Other findings are mainly represented by pulmonary injuries and injuries to the upper airways. Trachea and main bronchi might show lacerations or rupture while the lungs are often hyperinflated, hemorrhagic and marked by rib contusions. Barotrauma effects can also be detected in the gastrointestinal system: as for the lungs, there can be hemorrhagic injuries, large intestine avulsion or lacerations [10,11,[14][15][16][17][18].
The present study aims to better clarify a topic that has been poorly analyzed in the forensic medicine literature. Problems and forensic implications related to blast events are highlighted. A classification of the different scenarios resulting from explosions that the forensic pathologist might face is provided and schematically summarized in Table 2.

Materials and methods
A retrospective review of casualty data was conducted, selecting the titles and abstracts of articles based on their relevance. In the most common electronic databases (Pubmed, Scopus, Medline and Web of Science) a literature research was carried out using the following combination of free text protocols, individually and randomly combined trough the Boolean operator "AND": "explosion", "injury", "bomb", "explosive", "lesions", "terrorism", "suicide", "fatality", "burns", "identification", "disaster management". Filters such as full-text, publication date and English language were also activated. Preference was given to recently published articles, but commonly referenced and highly regarded older publications were also included. Moreover, the bibliographies of the selected articles were reviewed for other relevant articles. The research resulted in less than 50 articles,  • description of death scene investigations and forensic implications; • discussion of postmortem evidence at autopsy; • analysis of issues related to the interpretation of body injuries.
Additionally, pertinent forensic pathology handbooks and manuals [17][18][19][20][21], which include chapters dedicated to the description of explosion injuries, were used when appropriate. The cases concerning deadly explosions registered at the Institute of Legal Medicine of Milan were therefore examined. Subsequently, the selected studies were analyzed altogether.

Results
Thirty-four scientific articles dealing with forensic pathology and explosions that fulfilled the inclusion criteria were included in the present research. Among these were 18 case reports [5,13,[22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37], 11 original articles [7-9, 11, 12, 14-16, 38-40], 3 reviews [41][42][43], and 2 case series [44,45]. No technical notes or extensive reviews focused on forensic evaluation of explosions and implications were found. The analysis of the literature revealed that several authors had described, mainly through case reports, the most common alterations and modifications of the crime scene and also the postmortem injuries caused by different types of explosives. As previously mentioned, the presence of forensic pathologists on the crime scene and the subsequent medicolegal evaluation in the autopsy room may provide useful information when dealing with blast events. Alterations caused by blasts lead, if approached incorrectly, to misleading interpretations and faulty investigative reconstructions.
The following paragraphs discuss the problems and the challenges posed by the crime scene, as well as of the body effects of explosion on the body. Different situations related to explosion are described, starting from the on-site forensic examination and autopsy procedures. Each of the five scenarios related to explosions is introduced by the description of a past event that occurred in Milan. Technical procedures and scenarios are grouped in the following subsections: • on-site forensic examination; • autopsy procedures; • deaths by self-inflicted explosion; • domestic explosions; • work-related explosions; • heavy transport-related explosions; • terrorist events.

On-site forensic examination
The on-site investigation in the event of an explosion plays a twofold purpose: on one hand it allows identification of the victims, while on the other hand it contributes to clarify the circumstances which caused the explosion. Unfortunately, the presence of the forensic pathologist is not always requested from the very beginning of the rescue procedures: this may result in a confused and chaotic on-site examination, complicating further operations of identification. Explosion-related deaths are characterized by widespread injuries; the environment in which the explosion took place represents an important element that must be carefully considered by the forensic pathologist [16,20,46,47]. Firstly, the on-site examination is divided into either a closed or open system, as for the classification of mass disasters [8,15,46,47]. In a closed system the deflagration releases an amount of mechanical energy, also amplified by deflected and ricocheted pressure waves, provoking not only direct blast injuries but even the collapse of buildings such as industrial factories or houses ( Fig. 1) [46]. In an open system, such as squares, the countryside or out-of-town rural areas, blast waves can project bodies or parts of them across a large area (Fig. 2), and weather conditions, local predators and the natural decay of the dead bodies can destroy important elements and hinder forensic operations [16,20]. The whole area may be divided into smaller subareas which should be numbered in order to facilitate the recovery operations of the victims, or parts of them, including any personal items. In both cases, the explosion might result in a total obliteration of the affected area, thus preventing a thorough investigation of the scene, which is simply no longer recognizable [21,47]. Moreover, alterations of the crime scene can occur due to damaging effects of the area, which are usually followed by rescue operations [13,14].
As usual, during on-site investigations, the security assessment of the site should be performed before entering the area and at the same time the police should seal off the scene so that it will remain untouched and a proper investigation can take place [19,47]. First, it is important to take as many pictures as possible (videos are even more preferable) of the scene and the surrounding area. The conditions of the site may have been deeply modified by the fire or adverse natural events. If an appropriate computer program is used, the photographs and the scanning of the scene can provide a 3D recreation of the event and thus make it possible to accentuate important details for further forensic analyses [48]. In addition, an accurate photographic record of the victims and their visible injuries, including the location of the bodies and their distance from the explosive device must be realized [20,21].
It is important, especially for those cases of supposed atypical suicides, to give an answer to the questions asked by the local authorities so that the forensic investigations can be promptly organized. The forensic pathologist, supported by the police investigators, should obtain a detailed examination of the victim's background: a socio-economical and personal profile, such as prior medical history, is an essential documentation for a better interpretation of the crime's dynamics [15,21,22]. At the scene, the presence of any suicide note should be established, and information concerning any past suicide attempts be collected, including whether the victim possessed expertise in relation to explosives [22,23,44]. This investigatory approach can allow a preliminary reconstruction of the fatal event as a means of differentiating domestic accidents and homicidal explosionrelated deaths from such uncommon forms of suicide.
Finally, the analysis of the scene analysis should be performed by a specialized team with competent training in rescue procedures and death scene investigations: photographing and collecting the victim's clothes and personal items will be helpful for the following identification operations [14,16,38,41,42].

Autopsy procedures
In the event of an explosion-related death, a study protocol based on postmortem radiology, external examination, autopsy, forensic histopathology, toxicology and Fig. 1 An explosion caused by ANFO (Ammonium Nitrate Fuel Oil), which occurred in a closed domestic setting. In A decapitation of the victim's head (red arrows), and B a part of the scalp, which is completely avulsed from the face (red arrows). In C the face of the victim shows severe craniofacial damage, where a part of the scalp, the orbital cavities and the tongue can be still recognized (white arrows). Courtesy of Nucleo Investigativo Antincendi (NIA) Vigili del Fuoco Lombardia, Milan

Fig. 2
An explosion caused by LPG (liquefied petroleum gas), which occurred in an open system. The victim was working on an LPG storage tank (8,000 L), when a high-pressure gas leak hit his body. A The abdominal region is disrupted, with the liver lying on the ground (white arrows). B The left lower limb, which was found a few hundred meters away from the body. Courtesy of Nucleo Investigativo Antincendi (NIA) Vigili del Fuoco Lombardia, Milan anthropology is mandatory. Postmortem radiology should be the first forensic procedure performed on all bodies that have been involved in an explosion. It thereby offers the possibility to improve the observation of complex pathological findings in the autopsy room [49]. Bone and metallic fragments or other foreign objects may be easily detected at whole-body postmortem computed tomography (PMCT) [50]. Postmortem radiology may reveal small metal objects that form part of the explosive device mechanism. These may be invaluable in allowing the ballistics experts to recognize the handiwork of a particular bomb-maker or terrorist group [17]. Also, threedimensional surface scanning (3DSS) and multi-detector computed tomography (MDCT) are two techniques that are used in forensic medicine for digitalizing a body or body parts, such as bones. Interestingly, these radiological techniques allow reconstruction of severely injured skulls [51]. The instrumental approach plays two major roles in the event of an explosion: firstly, images of skeletal fractures can be evaluated whenever needed. Secondly, skull and facial digital reconstructions considerably improve identification of the victims [52,53]. Dental radiographs are also crucial information, since they are important for dental identification, postmortem profiling, and age estimations [54][55][56]. Nevertheless, a complete radiographic study before the autopsy allows preservation of information for subsequent evaluations; also, it provides evidence that can guide the forensic pathologist during the autopsy.
External examination is very useful in such cases, since features of the injury patterns can be defined, and several different samples can be collected as well. Thus, forensic pathologists should collect genetic swabs, if suspicious lesions are documented on the body. Homicides may indeed be concealed by the explosion of the building where the body was set. Ballistic experts may also collect bullets, projectile fragments and evidence from explosive discharge residues from the body of the victim. Explosive traces should be analyzed by a forensic chemical expert in order to define the typology of explosive material [57]. Moreover, residual particles may be searched through the use of Scanning Electron Microscopy/Energy Dispersive X-Ray Spectroscopy (SEM/EDX), which allows different classes of particles to be defined according to their composition and images [5,58]. Upon external examination, a thorough collection of personal items (e.g. clothes, jewels, identity cards), in association with the identification of personal descriptors such as tattoos, scars or medical devices should improve recognition of the victim. Also, the collection of fingerprints, if still available, may permit identification of the deceased.
Internal examination reveals macroscopic evidence of blast injuries. Particularly, lungs, and intestine blast injuries, major vessel lacerations and bone fractures are the most common pathological findings. Also, hyoid bone and thyroid cartilage fractures may be present. Histologic examination may help the forensic pathologist: in lungs, standard hematoxylin-eosin (HE) shows alveolar ruptures, thinning of alveolar septae, and enlargement of alveolar spaces. Other features include circumscribed subpleural, intraalveolar, and perivascular hemorrhages with a cufflike pattern. Also, Oil-Red-O (OR) histochemical staining may reveal fat embolism [12]. Furthermore, a moderate to strong hemoglobin immunoreactivity of the edema fluid within the alveolar spaces can be detected, showing a homogenous staining pattern. On the other hand, it is very important to establish the vitality of blast injuries. Thus, immunohistochemical analyses using anti-CD15, anti-IL-15 and anti-tryptase antibodies allow a precise assessment of the lesion that may have been involved in the determinism of death, representing a tool of considerable utility in explosion-related deaths [59]. Toxicological samples should also be collected; analysis may reveal toxic gases or substances inhaled or taken by the victim.
Finally, forensic pathologists should cooperate with forensic anthropologists and odontologists to improve personal identification of the victims. A dental examination carried out by a forensic odontologist is also a very important and effective method of identification, if antemortem data are available [38,[54][55][56]. Dental prosthesis records (fixed and removable prosthetic appliances as well) are one of the most common records used for identification purposes. Forensic odontologists may also analyze morphological features of extraoral and intraoral structures such as lip print patterns and rugae patterns, which are important elements for recognition of victims [60]. Genetic analysis on muscle or bone samples of the victims may confirm identity, if any relative is available.
A schematic forensic assessment in the event of an explosion is shown in Fig. 3.   Fig. 3 A forensic assessment to explosion-related deaths

Self-inflicted explosion deaths
In September 2006, an explosion occurred in an apartment building on the outskirts of Milan. The building collapsed and 4 people died. After the conclusion of the investigations, the cause of the explosive event was established as a gas leak provoked by a woman with suicidal intent.
In the analysis performed, three different patterns of non-terrorist explosion-related suicides can be defined, as follows: • The first pattern, which is the most frequent, shows a specific involvement of the cephalic region due to placement and detonation of fireworks or other explosive materials directly on the head or inside the oral cavity [22][23][24][25][26][27][28][29][30][31]. At autopsy, the injuries typically show a regular and symmetrical pattern, primarily consisting of severe craniofacial damages such as lacerations and blunt trauma of the lips, gums, palate, nose and orbits associated with extensive comminuted fractures of the mandible, maxilla and base of the cranium [22,26,44]. In addition, trauma can provoke deep cortical lacerations or pulpification of brain matter, albeit decapitation or complete head destruction have been rarely described [24,25]. Burns and gross tissue damages to the upper limbs, and, in particular, of the hand used for triggering the explosive device, provide possible evidence for suicide [27][28][29]. Other findings may include soft tissue and muscular hemorrhage of the neck, fracture and dislocation of the hyoid bone, lacerations and bruises of the lungs and the avulsion of the heart as a consequence of the direct effect of blast waves [22][23][24]. • The second pattern is identified in victims who clutch an explosive device in close proximity to the abdominal region. The injury pattern depends on the blast magnitude of the device. Siciliano et al. described the case of a 32-year-old man whose suicide was due to deflagration of a hand grenade which divided the body into two parts [32]. Similarly, Varga and Csabai reported the case of a 2v3-year-old man who located at the right side of the abdomen a homemade explosive device (a mixture of potassium cholate, sulfur and antimony) which caused the destruction of the heart, liver, diaphragm and lungs [33]. Tsokos et al. published the suicide of a 39-year-old man where death occurred in a combination of craniocerebral, abdominal and pelvic trauma with rupture of the large vessels as a result of a homemade bomb explosion [44]. • The third pattern involves cases which do not match the two patterns previously described. Particularly, an unusual suicidal explosive death of a 55-year-old male induced by intentional methane gas leak inside his bungalow was reported [34]; also, the case of 30-year-old man who was found dead in a truck cabin after the detonation and explosion of an acetylene cylinder [35]. In the first case, the cause of death was attributed to massive pulmonary fat embolism, extensive gastrointestinal epithelial burns, and acute respiratory distress syndrome (ARDS); while the second one resulted in charring and severe blast lung injuries.
Since in these cases victims who committed suicide by this uncommon manner were males, great caution should be paid by the forensic pathologist when dealing with a case of a woman suspected of a self-inflicted explosion death. A suspicion of murder or accident should be kept in consideration, until proven otherwise.
Further considerations are required in the evaluations of the damage patterns related to explosions, considering that such injuries can easily be mistaken for shotgun wounds. Hence, the absence of a firearm and the absence of any bullets at the scene, but the presence of radiopaque fragments within the body at the postmortem radiological examination, should direct suspicion towards the use of explosive devices [22,42,44]. Finally, an accurate assessment of the victim's background must be conducted in order to determine the manner of death: look for possible access to explosive materials, psychiatric disorders or any personal problems of the deceased [13,26,29,61].

In June 2016, a blast occurred in an apartment building in the south of Milan. After the explosion, the building collapsed, and 3 people died. Investigations identified the cause of the explosive event as a methane gas leak caused by a male saboteur with homicidal intentions.
This subcategory of explosion-related deaths includes fatalities by explosion that occurred in a domestic setting. Among these, accidental gas leak represents a common event worldwide [1,4,6,7,11,[17][18][19]46]. The scene is characterized by building collapse, fire, debris and a cloud of dust, which make rescue operations protracted and very difficult.
At autopsy, external examination can reveal a diffusely edematous body with multiple wounds [34], mainly lacerations, many abrasions along with different degrees of burns (Fig. 4). Internal examination usually shows pulmonary injuries and injuries to the upper airways: the trachea and main bronchi are lacerated while the lungs are often hyperinflated with severe blunt-force trauma and lacerations [1,4]. Also, traumatic asphyxia can frequently lead to death due to the massive amount of dust inhaled in the event of building collapse or to fixation of the chest by rubble [17][18][19]. The abdomen can be heavily damaged with organs torn off from the abdominal wall, such as the stomach, intestines and kidneys avulsion or hepatic and splenic gross lacerations [1,[17][18][19]]. An additional finding is the fracture of the thyroid cartilage (Fig. 5), which is produced by the explosive barotrauma or direct violent impact. When this evidence is found, attention must be paid to avoid misinterpretation regarding homicidal strangulation [11,17,22]. Autopsy helps to confirm the cause of death of the victims; also, it contributes to the assessment of the manner of death. Hence, a close cooperation between the forensic pathologists and the investigators can differentiate sabotage from accidents in cases of domestic explosive-related deaths.

Work-related explosions
In November 2010, an explosion occurred in the industrial area of a small town close to the metropolitan city of Milan. The point of blast origin was the storage site for hazardous waste due to a malfunction of safety systems. 4 workers died.
Work-related fatalities are a common phenomenon worldwide. Hazardous work is represented by coal mines (especially in the past for firedamp leaks), chemical industrial sites or firecracker factories, where explosions can typically occur [7,36,39]. In the literature, a few cases have been reported such as explosions caused by coffee-making machines, autoclave machines, or while repairing an air conditioner compressor [45]. A common pattern can be identified in the evidence that explosions derived from highpressure appliances [7,40,45].
Byard described the risk of work-related explosions in the enclosed engine room of fishing vessels if volatilized fuel is not adequately removed. The flammable properties of the mixture are determined by the ratio of fuel vapor to air: the speed and magnitude of combustion can be extremely Fig. 4 Bruises, abrasions, and different degrees of burns on the victim's body; furthermore, dust can be seen on the face and the hair of the victim. The woman was involved in a domestic explosion, which caused the collapse of a building Fig. 5 Blast effects on the larynx resulting in a deep laceration of the thyroid and cricoid cartilages. This injury was caused by a domestic explosion related to a gas leak violent, resulting in an explosion [43]. Furthermore, an unusual "work-related" death occurred in Milan in 2017, when a thief died while attempting to break into an automated teller machine (ATM) using the explosive power of an acetylene gas cylinder [37]. Most of the cases were accidental events; moreover, victims frequently died after the explosive accident or within the first 24 h, reflecting the seriousness of the injuries reported by the workers [40]. The injury pattern showed a combination of blast effects, burns, and flying missile injuries which damaged internal organs. The lungs may also show subpleural patchy hemorrhages, pulmonary contusions and intrapulmonary hemorrhage (Fig. 6). The predominant site of lethal wounding was the head, followed by the chest or the combination of both [36,37,40,43,45].
In cases like the ones mentioned above, forensic examination of the dead bodies revealed mainly several burn injuries: different degrees of burns are differently distributed all over the body, including significant charring. Charred and extensively damaged bodies require a careful examination for identification purposes [20,21,41]. Nowadays, workplace deaths represent an important social problem [40]: the reconstruction of the event allows authorities to find out which responsibilities involved the employer and which ones the worker.

Heavy transport-related explosions
In October 2001, two aircrafts collided on the runway at Milan city airport "Linate". The crash occurred in weather conditions of heavy fog and was caused by nonfunctioning and nonconforming safety systems. After the collision, a fire explosion was produced, resulting in 118 deaths and no survivors.
A variety of events can lead to explosions related to large transport vehicles. Aircraft accidents, such as the example reported above, railway disasters, maritime accidents and other mass disasters can be included in this category [20]. In these cases, the explosion produces a significant kinetic effect as a result of collisions between objects travelling at high speed [19]. Since the mechanical shock wave cannot dissipate the energy of the impact itself, the accumulation of heat and pressure at the contact site instantly breaks the two objects, projecting their fragments in every direction and vaporizing or partially melting the material [7,38]. These events result in a wide number of fatalities, the remains of which, being often severely traumatized, charred and/or dismembered, need to be identified and subjected to medicolegal investigation [1,2,13,20].
Two major factors complicate the recovery of human remains: body destruction and context [40]. Since explosion may lead to extreme body fragmentation, care must be taken in analysis at the scene [15,19,47]. Forensic analysis of remains must include assessment of minimum number of individuals and commingling issues as well as identification [17,18,54,55,56]. Identifications are frequently facilitated by recovery and analysis of identification tags, dental restorations and surgical materials [41]. The context is important since in most cases human remains are located in proximity to other materials of similar appearance [20,21]. Ideally, forensic anthropologists should participate in the recovery since they are experts in recognizing fragmented human remains [17,41]. In the case mentioned above, the team of experts guided by forensic odontologists succeed identifying all the victims, through consultation of the victims' dental records, a fundamental part of the entire investigation [38,60].
At autopsy, trauma interpretation can be difficult due to the multiplicity of skeletal and visceral injuries with a prevalence of heat-related lesions, usually complete charring and fragmentation [41]. The distinction of different injury patterns may provide important information for the investigators, since one of the priorities is to reconstruct the dynamic of the event to differentiate accidental events from sabotage.

Terrorist events
In December 1969, during a terrorist attack, a dynamite bomb exploded at the headquarters of Banca Nazionale dell'Agricoltura, a few hundred meters from the Duomo of Milan. 17 people were killed and 88 wounded. On the same day, a second unexploded bomb was found.
In cases of explosive military devices, blast effects uncommonly lead to death since the terrorist bomb usually produces low energy barotrauma in close proximity to the explosion. For more powerful bombs, flash burns can occur on individuals nearby [2,3].
The individual in contact with or near the seat of explosion can be blown to pieces and scattered by the force of explosion gases [8]. Solid fragments from the device, such as the case or the conveyance, in which the bomb was concealed (a car usually is usually the most common vehicle used), can be projected at high speed and over a wide area, hitting the bodies of people nearby [2]. Metallic pieces can act like bullets, causing significant or fatal injuries [13,18]. In these cases, the autopsy shows a multiplicity of injuries such as a pigmented dust produced by the explosion on skin and clothing, the burning of hair and eyebrows, and the laceration and ignition of clothing [10,21]. Also, the so-called "peppering" appearance of the skin caused by the small missiles creating the typical triad of lesions (bruises, abrasions and lacerations) can be assessed (Fig. 7) [9,17]. The body can be totally or partially destroyed: extremities are equally involved resulting in hands, arms, feet and legs disrupted or ripped out (Fig. 8). Localized injuries at the legs and the abdominal region have been assessed on the body of the terrorist when a premature deflagration occurs [8,14,42]; it can be an important element to be considered in order to distinguish between bombers and suicidal attacks. The number and distribution of fractures can help the differentiation of injury mechanisms, as they are relevant criteria for distinguishing an explosive charge carrier from a victim [13,18,42]. According to the literature, multiple injuries resulted in the cause of death, followed by localized injuries at the head, then the chest and the abdomen [6,8,14].
Various ballistic and forensic experts may be involved in terrorist events; among them, chemists and engineers will have the task of determining the composition and position of the device. The forensic pathologist can be part of the event reconstruction, as localized severe trauma indicates the relative position of the bomb and the victim at the time of detonation [14]. Trace evidence of explosive residues can be found on the bodies and further analyzed [5]. In this way, investigations can receive a fundamental support in identifying also the bomb-carrier and, thus, the precise dynamics of the event [17,18,42]. In the case mentioned above, starting from the autopsy reports, the team of experts in charge managed to develop a visual record placing each victim in the most probable position at the time of the explosion, playing a pivotal role in the forensic proceedings. Fig. 7 Peppered appearance of the skin caused by the small missiles creating the typical triad of bruises, abrasions and lacerations. These injuries were caused by an explosive device (trinitrotoluene), which was hidden in a car

Conclusions
Explosion-related deaths are a critical issue for forensic pathologists since a proper investigative reconstruction and interpretation are based on many different pieces of information. Hence, this review shows that death caused by explosions is an uncommon event, with the majority of cases occurring in the workplace and in a domestic setting. Non-terrorist self-inflicted deaths are exceptional, especially if they are associated with the detonation of the explosive in close proximity to the head and abdomen. Non-terrorist homicides using explosive devices are even less common. Moreover, it is important to be aware that the integrity of both the scene and the victim's body may be damaged by alterations due to human and environmental factors.
Once on-site forensic examination is completed, forensic pathology investigations should therefore rely on study protocols based on precise procedures for postmortem analysis. Postmortem radiology allows the preservation of information for subsequent evaluations; also, it provides evidence that can guide the forensic pathologist during the autopsy. This approach improves the analysis of complex pathological findings of blast injuries. Moreover, forensic histopathology, toxicology, genetics, chemistry, and anthropology are key elements to achieve successful outcomes. The correct training for a forensic pathologist should include expertise regarding death produced by blasts, preferably through crime scene simulations to avoid misinterpretations and evaluation errors. Forensic and emergency teams must know the patterns of the injuries caused by explosives. Ideally, a proper and structured postmortem assessment in explosion-related cases must observe the phases summarized as follows: • on-site forensic investigation; • postmortem radiological examination; • external examination to gather surface evidence (genetics, chemistry, ballistics), collect clothes and items, and note personal descriptors and document traumatic injuries; • internal examination to analyze both macroscopic pathological findings of blast injuries and other traumatic lesions; also, the autopsy allows samples to be collected for histopathological, toxicological, anthropological, odontological and genetic analyses.
In conclusion, a multi-disciplinary approach is highly recommended in death investigations. Especially, when dealing with explosion-related deaths, a close cooperation between forensic pathologists and other forensic professionals, such as anthropologists, radiologists, engineers, ballistic experts, and chemists is preferable, in order to provide a high-quality forensic investigation and consequent evaluation.

Key points
1. Forensic pathologists may deal with different and complex explosion-related scenarios. 2. For blast injuries, postmortem radiology before autopsy examination is mandatory. 3. Identification of explosive residue particles requires ballistic and chemistry experts. 4. Blast injuries typically involve the lungs, intestine, and major vessels. 5. Forensic anthropologists and odontologists may help the pathologist to identify victims of explosions. Fig. 8 Peppered appearance of the skin with the lower extremities partially disrupted. These injuries were caused by an explosive device (trinitrotoluene), which was hidden in a car Funding Open access funding provided by Università degli Studi di Milano within the CRUI-CARE Agreement. The authors received no financial support for the research, authorship, and/or publication of this article.

Conflict of interest
The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Ethical approval: This study follows the guidelines provided by Legislation and the National Bioethical Committee.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.


An automatic procedure for crop mapping using agricultural monitoring

IoT and Machine Learning are emerging techniques used in existing days. Agriculture plays a vital role in human survival. Mapping crop according to the current environment is important to improve agriculture. pH sensors, dielectric soil moisture Sensors, mechanical sensors, optical sensors, electro-chemical sensors and air flow sensors are used in this proposed system to gather data about the soil and supervised Learning associated with Association algorithm are used to analyze and predict which crop maps to the soil in the present circumstances. Ensemble Technique is integrated to make accurate classification to select the type of crop. Technology combining both IoT and Machine learning are used to improve the production of the crop which proportionally helps to improve the agricultural yield.

Introduction
IoT: Internet of Things is the internet-based technology which uses sensors to collect data and it is sent to IoT Analysis platform which accumulates the data and makes the decision. The workflow of IoT is shown in the Figure 1.  In this system pH Sensors with various types of sensors are applied. All the sensor nodes are inserted in the agricultural land and they gather data by sensing the soil. The sensor provides smart farming, it assists farmers in making a decision. Some of the sensors are, x pH Sensors in crop growing: Sensor which inform about nutrient in the inserted soil and also it lists out unnecessary chemical molecules in the soil, by this we can get an idea about soil quality, which helps to make a decision about what crop can be cultivated according to its nutrient values. Figure 2 and Figure 3 Shows the pH Sensor and Dielectric Soil Moisture Sensors.  x Optical Sensors: These sensors are used to verify types of soil using light and also additional to that it informs about various organic matters and moisture inside the soil. x Electrochemical Sensors: These sensors are used as assistance in gathering soil chemical information by sensing various ions in the soil. x Mechanical Sensors: In crop production soil compaction can measure by these sensors and also the able to mechanical resistance. x Dielectric Soil Moisture Sensors: It helps in measuring humidity and moisture levels in the soil by the help of calculating dielectric steady of the soil. It also gives information about the water level, the surface temperature in the soil x Types of Soil used for agriculture: Identifying the type of soil is an essential one which helps to predict the suitable crop for that soil. Soil is classified into many types., x Clay earth: The property content is very high and it is sticky. The dimension of soil s takes away as 0.2mm. It is wealthy in natural substance. It was appropriate for crop cultivation like paddy and rice. x Loamy Soil: It contains sand, clay, and sediment. It has sufficient hummus. From this soil sugarcane, wheat, jute, cotton, oilseed vegetables and pulses can be produced. x Red soil: It has more iron oxide. It has a lot of water content and has a fusion of clay and sand, Machine Learning: In Machine Learning the Machine Analyses, learns past data to make Decision and Predicts the output. There are four types of Machine learning which provide a useful way to classify, they are Supervised Learning, Unsupervised Learning, and Reinforcement Learning. In our proposal we use a Supervised learning algorithm to classify the soil type to associate the suitable crop to grow. Supervised learning which generalizes and classifies targets based on this training set.
Association Algorithm: In large dimensional dataset, finding interesting useful hidden patents are more interesting. Researchers can association rule identify relations among variables. It produces strong rules using support and confident measures to claim the system. Here it maps soil and crop according to the molecules present in the soil. It helps more in forming smart farming. The accuracy makes more than 90% of the result in crop cultivation by using sensors and machine-learning technology.

Related work
Vishal Meshram et l. [1] focused on the analysis review about how the machine learning technique improves crop yield. Nguyen-Thanh Son [2] explained how the random forest and SVM technique approach helps to decide to predict the yield of the crop, he mainly focused on rice crop in Taiwan. Laura Dingle Robertson [3] determines how to make a preference of crop type by collecting data about soil stipulation and he categorizes the outcomes of his result. Papa Sai Venkat [4] states the enhanced stacking regression techniques and their vital role in improved prediction of accurate crop yield.
Hassan Afzaal et al. [5] focused on the improvement of potato yield in Atlantic Canada employing various supervised learning algorithms. Anu Bala [6] enumerates less expensive and most comprehensive, a sophisticated procedure that enables improved harvest and surroundings situation evaluation and choice making. Manjula Josephine [7] Explained how ML helps in the production of crops. Ayazf et al. [8] highlights the prospective of IoT in farming and various challenges issue faced while appending the new knowledge through conventional cultivation practices. Kushagra Agrawal [9] narrated how IoT technology helps in less consumption of resources with more productivity of crop that solve cultivation issues by identifying the suitable occasion of seeding of crops. Ganthimathi [10] enumerates how the Feed Forward Neural Network helps to detect the Plant Disease. G Kavitha [11] tells about smart farm improvement in yield by priory identifying plant syndrome and Soil Quality. Muthaiah and Balamurugan [12] provided a solution for determining the yield of the crop by means of the Random Forest algorithm. Andrew Crane-Droesch [13] suggests a deep neural network for improving cultivation. Robert Jana, Mallikarjun B.C [14] Implemented IoT Technology to supervise the significance of soil quality and type. Ashok Tatapudi, P Suresh Varma [15] analyze humidity, moisture, and temperature status of the soil using a sensor device by using ML Algorithms. Erik et al. [16] found out famine monitoring system for moisture in the soil. Mazaheriet al. [17] Show how soil humidity affects crop yield.
P. E. Francis et al. [18] implemented the assessment of moisture deficits of soil. Ranjini Guruprasad et al. [19], study tells about complete investigation of new machine learning technique with relevant features to improve the performance of the crop yield. Mhudchuay et al. [20] focused by means of deep Q-learning to discover the best dealings for rice cultivation, at this juncture they represent the past climatic statistics to predict the yield, and employ the deep Q-learner to choose at what time to begin cultivation and what time to harvest to maximize the income. Marion Olubunmi Adebiyi et al. [21], tells about machine learning (ML)-aided transportable scheme for farmland optimization, make use of a variety of inputs such as place, crop category, mud variety, soil pH, and spacing.

Proposed Architecture
It represents how the proposed system works, x Sensors are installed in the soil and it used to gather data from the soil.
x Data is analyzed by the IoT processing module x The analyzed data are given to the ML techniques. x SVM classifier is exploited to classify the type of soil based on moisture, organic, and humidity value of the soil collected from sensors. x The parameter of the soil is analyzed and a suitable crop is selected by using the association Apriori algorithm. x Ensemble Learning is used for the accurate selection of a suitable crop. The workflow is represented by Figure 5.

Task performed in the systems:
The above Figure 6 shows how the framework is done in the proposed work were, then used by the help of IoT technology choose the suitable crop, Here first sensors are inserted in the land and the data collected by the sensors are analyzed and the parameter is monitored based on moisture, humidity, temperature, PH, and Rainfall at that present situation. Machine learning algorithms are used to classify the soil type and an ensemble learner is used to select a suitable crop for cultivation. here combining IoT and Machine learning algorithms used to form smart farming which leads to 90% of the success rate in cultivation, the

Implementation Results
The graph shown in Fig 8 is the visualization of the result collected by the Sensors that are installed in agricultural land Kaligoundanpalam, Namakkal district and the analyzed value of Rainfall in (mm) and Temperature in (C) for all the months of the year had taken and moisture % and humidity of the soil is also measured and taken for implementation and the analyzed data given to ensemble Learner and suitable crop type example Rice crop is selected in the month of November through this result and which provide the higher success rate of production of the rice crop.

Conclusion and Future Enhancement
Machine Learning technique with IoT plays a vital part in recent days, it is also employed in the agriculture field to improve the throughput. Agriculture plays a vital role so mapping crop according to climate is important therefore various sensors added with IoT technology are used in this proposed system to gather data and the results shown in the above graph and they are analyzed and according to the data analyzed crop is mapped to the soil. Ensemble Technique is integrated in this study makes accurate classification to select the type of crop. Technology combining both IoT and Machine learning is used to improve the production of the crop which proportionally helps to improve the agricultural yield and for hit rate and performance improvement in the future deep learning model can be used.