Imaging informatics, also known as radiology informatics or medical imaging informatics, is a subspecialty of biomedical informatics that aims to improve the efficiency, accuracy, usability and reliability of medical imaging services within the healthcare enterprise. It is devoted to the study of how information about and contained within medical images is retrieved, analyzed, enhanced, and exchanged throughout the medical enterprise.
As radiology is an inherently data-intensive and technology-driven specialty, those in this branch of medicine have become leaders in Imaging Informatics. However, with the proliferation of digitized images across the practice of medicine to include fields such as cardiology, ophthalmology, dermatology, surgery, gastroenterology, obstetrics, gynecology and pathology, the advances in Imaging Informatics are also being tested and applied in other areas of medicine. Various industry players and vendors involved with medical imaging, along with IT experts and other biomedical informatics professionals, are contributing and getting involved in this expanding field.
Imaging informatics exists at the intersection of several broad fields:
Due to the diversity of the industry players and broad professional fields involved with Imaging Informatics, there grew a demand for new standards and protocols. These include DICOM (Digital Imaging and Communications in Medicine), Health Level 7 (HL7), International Organization for Standardization (ISO), and Artificial Intelligence protocols.
Current research surrounding Imaging Informatics has a focus on Artificial Intelligence (AI) and Machine Learning (ML). These new technologies are being used to develop automation methods, disease classification, advanced visualization techniques, and improvements in diagnostic accuracy. However, AI and ML integration faces several challenges with data management and security.
History.
Medical imaging to imaging informatics.
While the field of imaging informatics is based around the power of modern computing, its roots trace back to the dawn of the 20th century. On November 8, 1895, German physicist Wilhelm Conrad Röntgen observed a new imaging technique he coined “X-rays” during his experiments. This discovery led to the creation of the medical imaging field, and in turn launched a new wave of human innovation.
X-rays stood as the only medical imaging technology for several decades following its discovery. However, the arrival of the mid 20th century meant the expansion of the medical imaging field. The new modalities included: computed tomography (CT) to visualize soft tissue with a high degree of resolution; Magnetic Resonance Imaging (MRI) which is a modern standard for soft tissue imaging; Ultrasound that uses sound waves to create less expensive visualizations; Nuclear Imaging and Hybrid Scanners for functional imaging and imaging with higher spatial resolution created by combining multiple modalities.
As these imaging techniques became more sophisticated, the amount of information that medical imaging professionals were expected to process also increased. Additionally, the digital revolution of the mid to late 20th century further increased the data these techniques could gather. As a result, the main limiting factor for the medical imaging field became the human inability to accurately interpret large amounts of data. Thus, the need arose for computerized assistance with complex digital imaging analysis, storage and manipulation. Modern Imaging Informatics was developed to fulfill these needs.
Imaging informatics development.
Imaging Informatics is a broad field with numerous areas of interest, making its development a culmination of the development of various individual technologies. Several of the key innovations for the field are as follows:
Picture archiving and communication system (PACS).
The development of PACS popularized the use of image storage and retrieval systems in medical practices. Moreover, this new technology demanded the development of others. The world quickly realized that digital imaging standards would need to be put in place given the impact PACS had on the medical community. The American College of Radiology (ACR) and the National Electrical Manufacturers Association (NEMA) created the Digital Imaging and Communications Standards Committee (later becoming DICOM) in response to this concern.
Information technology integration.
The digital age’s impact on radiology resulted in a large influx of data that needed to be managed. To combat this, the field of information technology was incorporated with technology such as Radiology Information System (RIS) and Hospital Information System (HIS). These systems would work in tandem with PACS and other imaging technology to streamline the patient data management, as shown in the figure to the right.
Computer-aided detection and diagnosis.
The idea of computer-aided detection (CAD) and computer-aided diagnosis (CADx) is that the process of analysis and interpretation of medical image data could be automated, with a potentially higher degree of accuracy than human detection and diagnosis. Interest in this subject dates back to 1966, when radiology imaging first became digitized. The first successful implementation of a CAD system was in 1994 at the University of Chicago for use in mammography. This was followed by the first commercial CAD system in 1998 called ImageChecker M1000. With the arrival of the 21st century, machine learning techniques have been utilized to accomplish a version of the CAD and CADx systems. The future development of these technologies is advantageous as it gives a solution to human limitations in medical image processing. Although a highly accurate and fully automated CAD system has yet to be realized, recent advancements in Artificial Intelligence may allow for functioning implementations.
Standards and protocols.
In the domain of imaging informatics, it is imperative to ascertain that the information pertaining to industry standards and data-sharing protocols is contemporaneous. The expeditious advancement in this field necessitates a vigilant approach to sustain uniformity, foster interoperability, and guarantee the efficacious dissemination of imaging data. To this end, several pivotal facets warrant rigorous consideration:
Digital imaging and communications in medicine (DICOM) standards.
The Digital Imaging and Communications in Medicine (DICOM) standard delineates a sophisticated structural schema that integrates medical imaging data with pertinent patient identifiers into unified data sets, analogous to the embedded metadata in JPEG images. Such DICOM entities are constituted by a multitude of attributes, notably encapsulating pixel data, which in certain imaging modalities, corresponds to discrete images or, alternatively, an array of frames exemplifying kinetic or volumetric data, as observed in cine loops or multi-dimensional scans in nuclear medicine. This architecture accommodates the assimilation of intricate, multi-faceted data into a monolithic DICOM file. The standard accommodates a spectrum of pixel data compression algorithms, including but not limited to JPEG and JPEG 2000, and provisionally allows for holistic data set compression. DICOM specifies three encodings for data elements, with a predilection for explicit value representations, barring specific exceptions as elaborated in Part 5 of the DICOM compendium. Uniformly applied across diverse applications, the file manifestation customarily incorporates a header that houses essential attributes and data on the originating application.
The proposed workflow integrates the use of DICOM Structured Reporting (SR), in which essential measurements are encoded as DICOM SR objects. These objects are then used to fill a predefined SR template, resulting in the creation of a standardized report composed of discrete data elements. This report is subsequently transmitted to the Electronic Medical Record (EMR) system. The discrete data extracted from these reports facilitate the longitudinal monitoring of individual patient metrics, are forwarded to data registries, or are leveraged for clinical research purposes.
Health level 7 (HL7) standards.
DDInteract has been crafted to enhance cooperative engagement between healthcare practitioners and patients, aiming to ascertain the optimal therapeutic approach that minimizes the hazards posed by potential drug-drug interactions. The user interface of DDInteract is systematically organized into four distinct segments.
Medication data can be represented across a variety of Fast Health Interoperability Resources (FHIR) resources, necessitating careful analysis by DDInteract. Specifically, MedicationRequest is utilized for medications prescribed to the patient; MedicationDispense covers medications that have been physically provided to the patient; and MedicationStatement pertains to medications that the patient reports having taken or is currently taking. It is possible for a single medication to be represented in multiple resource forms, with potential redundancies being amalgamated into a single record based on the most recent date and a defined hierarchy among the resource types.
To optimize the efficiency of data retrieval from the FHIR server, not every instance of medication is considered. Only those resources that are currently active or were active within the past 100 days are included, adhering to the prevalent U.S. protocol that typically allows for medication dispensation for a duration not exceeding three months.
International organization for standardization (ISO) standards.
A Quality Management System (QMS) is an integrative construct that includes the organizational architecture, the allocation of resources, the expertise of personnel, and the repository of documents and procedures that collectively contribute to the assurance and enhancement of quality in an entity's offerings. It delineates a suite of systematically orchestrated actions essential for governing and optimizing quality parameters. The ISO 9000 suite emerges as the preeminent and universally endorsed schema for QMS implementations, whereas the ISO 15189 standard provides a specialized framework designed expressly for the exigencies of clinical laboratory settings.
Artificial intelligence in imaging informatics.
A systematic review critically assessed the design, reporting standards, risk of bias, and validity of claims within studies that compare the efficacy of diagnostic deep learning algorithms in medical imaging against the expertise of clinicians. Conducted using data from prominent databases spanning from 2010 to June 2019, the review specifically targeted studies involving convolutional neural networks (CNNs)—notable for their capacity to autonomously discern crucial features for image classification within medical contexts. The investigation uncovered a notable deficiency in randomized clinical trials concerning this subject, identifying only ten such studies, of which merely two were published, exhibiting low risk of bias and commendable adherence to reporting protocols. Among the 81 non-randomized studies located, a minority were prospective or validated in practical clinical settings, with the majority presenting a high risk of bias, substandard compliance with reporting norms, and a pronounced lack of accessibility to data and code. This review underscores the imperative for an augmentation in the number of prospective studies and randomized trials, advocating for diminished bias, amplified clinical pertinence, enhanced transparency, and tempered conclusions in the burgeoning field of applying deep learning to medical imaging.
The exponential growth in digital data alongside enhanced computing capabilities has markedly accelerated advancements in artificial intelligence (AI), which are now progressively being incorporated into healthcare. These AI applications aim to refine diagnosis, treatment, and prognosis through sophisticated classification and prediction models. Nevertheless, the evolution of these technologies is impeded by a lack of rigorous reporting standards relating to data sourcing, model architecture, and the methodologies employed in model evaluation and validation. In response, we propose MINIMAR (Minimum Information for Medical AI Reporting), an initiative designed to establish critical parameters for understanding AI-driven predictions, the demographics targeted, inherent biases, and the ability to generalize these technologies. We urge the adoption of standardized protocols to ensure that AI implementations in healthcare are reported with accuracy and responsibility, facilitating the development and deployment of associated clinical decision-support tools while simultaneously addressing critical concerns regarding precision and bias.
As a foundational requisite, the proposed standard ought to fulfill several essential criteria: Firstly, it should encompass comprehensive details concerning the population from which the training data are derived, delineating the sources of data and the methods employed for cohort selection. Secondly, the demographics of the training data should be explicitly documented to facilitate a substantive comparison with the demographic characteristics of the population on which the model is intended to operate. Thirdly, there should be a thorough disclosure of the model’s architecture and its development process to allow for a clear interpretation of the model's intended purpose, comparison with analogous models, and to enable exact replication. Fourthly, the process of model evaluation, optimization, and validation must be transparently reported to elucidate the means by which local model optimization is attained and to support replication and the sharing of resources.
Evaluation of artificial intelligence in imaging informatics.
Recommendations for future development.
In summary, while AI offers significant opportunities for advancing imaging informatics, leveraging these opportunities to their fullest extent necessitates stringent validation, adherence to robust reporting frameworks, and an overarching commitment to addressing ethical considerations. These steps are pivotal in ensuring that AI-driven tools achieve their promise of enhancing efficiency and effectiveness in medical diagnostics.
Areas of interest.
Key areas relevant to Imaging informatics include:
Applications.
Imaging Informatics has quite a few applications within the medical field.
Radiology.
Imaging Informatics is most prominent within the field of radiology. Using AI, radiologists can use Imaging Informatics to ease their job and save time whilst analyzing images. A study published in "Current Medical Imaging" discovered that in CT imaging assisted by AI, the reading time to detect lung nodules and pleural effusions was reduced by more than 44% for radiologists.
Cardiology.
Imaging informatics within Cardiology aids in