Imaging informatics

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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.^[1] 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:

• biological science – includes bench sciences such as biochemistry, microbiology, physiology and genetics
• clinical services – includes the practice of medicine, bedside research, including outcomes and cost-effectiveness studies, and public health policy
• information science – deals with the acquisition, retrieval, cataloging, and archiving of information
• medical physics / biomedical engineering – entails the use of equipment and technology for a medical purpose
• cognitive science – studying human computer interactions, usability, and information visualization
• computer science – studying the use of computer algorithms for applications such as computer assisted diagnosis and computer vision

Standards and Protocols[edit]

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:

DICOM (Digital Imaging and Communications in Medicine) Standards[edit]

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 utilized to fill a predefined SR template, resulting in the creation of a standardized report comprised 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.^[2]

Health Level 7 (HL7) Standards[edit]

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[edit]

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.^[3]

Artificial intelligence in Imaging informatics[edit]

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.^[4]

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.^[5]

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.^[6]

Table: Reporting standards for Model evaluation of artificial intelligence solutions in health care ^[7]

Areas of interest[edit]

Key areas relevant to Imaging informatics include:

• Picture archiving and communication system (PACS) and component systems
• Imaging informatics for the enterprise
• Image-enabled electronic medical records
• Radiology Information Systems (RIS) and Hospital Information Systems (HIS)
• Digital image acquisition
• Image processing and enhancement
• Radiomics
• Image data compression
• 3D visualization and multimedia
• Speech recognition
• Computer-aided diagnosis (CAD).
• Imaging facilities design
• Imaging vocabularies and ontologies
• Data mining from medical images databases
• Transforming the Radiological Interpretation Process (TRIP)^[8]
• DICOM, HL7, FHIR and other standards
• Workflow and process modeling and process simulation
• Quality assurance
• Archive integrity and security
• Teleradiology
• Radiology informatics education
• Digital imaging

Training[edit]

In the US and some other countries, radiologists who wish to pursue sub-specialty training in this field can undergo fellowship training in imaging informatics. Medical Imaging Informatics Fellowships are done after completion of Board Certification in Diagnostic Radiology, and may be pursued concurrently with other sub-specialty radiology fellowships.

The American Board of Imaging Informatics (ABII) also administers a certification examination for Imaging Informatics Professionals. PARCA (PACS Administrators Registry and Certification Association) certifications also exist for imaging informatics professionals.^[9]

The American Board of Preventive Medicine (ABPM) offers a certification examination for Clinical Informatics for physicians who have primary board certification with the American Board of Medical Specialties, a medical license and a medical degree. There are two pathways to be eligible to sit for the examination: Practice Pathway (open through 2022) for those who have not completed ACGME-accredited fellowship training in Clinical Informatics and ACGME-Accredited Fellowship Pathway of at least 24 months in duration.^[10]

References[edit]

External links[edit]

• The Society for Imaging Informatics in Medicine
• American Board of Imaging Informatics