Ontologies in the New Computational Age of Radiology: RadLex for Semantics and Interoperability in Imaging Workflows.

Journal: Radiographics : a review publication of the Radiological Society of North America, Inc
Published Date: Mar 1, 2023

Abstract

From basic research to the bedside, precise terminology is key to advancing medicine and ensuring optimal and appropriate patient care. However, the wide spectrum of diseases and their manifestations superimposed on medical team-specific and discipline-specific communication patterns often impairs shared understanding and the shared use of common medical terminology. Common terms are currently used in medicine to ensure interoperability and facilitate integration of biomedical information for clinical practice and emerging scientific and educational applications alike, from database integration to supporting basic clinical operations such as billing. Such common terminologies can be provided in ontologies, which are formalized representations of knowledge in a particular domain. Ontologies unambiguously specify common concepts and describe the relationships between those concepts by using a form that is mathematically precise and accessible to humans and machines alike. RadLex is a key RSNA initiative that provides a shared domain model, or ontology, of radiology to facilitate integration of information in radiology education, clinical care, and research. As the contributions of the computational components of common radiologic workflows continue to increase with the ongoing development of big data, artificial intelligence, and novel image analysis and visualization tools, the use of common terminologies is becoming increasingly important for supporting seamless computational resource integration across medicine. This article introduces ontologies, outlines the fundamental semantic web technologies used to create and apply RadLex, and presents examples of RadLex applications in everyday radiology and research. It concludes with a discussion of emerging applications of RadLex, including artificial intelligence applications. RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Authors

  • Leonid L Chepelev
    From the Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto General Hospital, 585 University Ave, 1-PMB 286, Toronto, ON, Canada M5G 2N2 (L.L.C.); Insygnia Consulting, Toronto, ON, Canada (D.K.); Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA (C.E.K.); Department of Radiology, MedStar Georgetown University Hospital, Washington, DC (R.W.F.); and Imaging Service, Baltimore VA Medical Center, Baltimore, MD, and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD (K.C.W.).
  • David Kwan
    From the Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto General Hospital, 585 University Ave, 1-PMB 286, Toronto, ON, Canada M5G 2N2 (L.L.C.); Insygnia Consulting, Toronto, ON, Canada (D.K.); Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA (C.E.K.); Department of Radiology, MedStar Georgetown University Hospital, Washington, DC (R.W.F.); and Imaging Service, Baltimore VA Medical Center, Baltimore, MD, and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD (K.C.W.).
  • Charles E Kahn
    Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, USA.
  • Ross W Filice
    MedStar Health, MedStar Georgetown University Hospital, 3800 Reservoir Rd, NW CG201, Washington DC, 20007 (R.W.F.); and MedStar Health, National Center for Human Factors in Healthcare, Washington, DC (R.M.R.).
  • Kenneth C Wang
    From the Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto General Hospital, 585 University Ave, 1-PMB 286, Toronto, ON, Canada M5G 2N2 (L.L.C.); Insygnia Consulting, Toronto, ON, Canada (D.K.); Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA (C.E.K.); Department of Radiology, MedStar Georgetown University Hospital, Washington, DC (R.W.F.); and Imaging Service, Baltimore VA Medical Center, Baltimore, MD, and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD (K.C.W.).

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