Exchange of Quantitative Computed Tomography Assessed Body Composition Data Using Fast Healthcare Interoperability Resources as a Necessary Step Toward Interoperable Integration of Opportunistic Screening Into Clinical Practice: Methodological Development Study.

Journal: Journal of medical Internet research
Published Date: May 21, 2025

Abstract

BACKGROUND: Fast Healthcare Interoperability Resources (FHIR) is a widely used standard for storing and exchanging health care data. At the same time, image-based artificial intelligence (AI) models for quantifying relevant body structures and organs from routine computed tomography (CT)/magnetic resonance imaging scans have emerged. The missing link, simultaneously a needed step in advancing personalized medicine, is the incorporation of measurements delivered by AI models into an interoperable and standardized format. Incorporating image-based measurements and biomarkers into FHIR profiles can standardize data exchange, enabling timely, personalized treatment decisions and improving the precision and efficiency of patient care.

Authors

  • Yutong Wen
    Data Integration Center, Central IT Department, University Hospital Essen, Essen, Germany.
  • Vin Yeang Choo
    Data Integration Center, Central IT Department, University Hospital Essen, Essen, Germany.
  • Jan Horst Eil
    Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Essen, Germany.
  • Sylvia Thun
    Charité Universitätsmedizin, Berlin Institute of Health, Germany.
  • Daniel Pinto Dos Santos
    Department of Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany.
  • Johannes Kast
    Mint Medical GmbH (a Brainlab company), Heidelberg, Germany.
  • Stefan Sigle
    MOLIT Institute, Heilbronn, Germany.
  • Hans-Ulrich Prokosch
    Institute for Medical Informatics, University Erlangen-Nuremberg, Erlangen, Germany; Center for Medical Information and Communication, Erlangen University Hospital, Erlangen, Germany.
  • Diana Lizzhaid Ovelgönne
    Siemens Healthineers AG, Forchheim, Germany.
  • Katarzyna Borys
    Institute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstraße 2, 45131 Essen, Germany; Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147 Essen, Germany. Electronic address: Katarzyna.Borys@uk-essen.de.
  • Judith Kohnke
    Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.
  • Kamyar Arzideh
    Data Integration Center, Central IT Department, University Hospital Essen, Essen, Germany.
  • Philipp Winnekens
    Data Integration Center, Central IT Department, University Hospital Essen, Essen, Germany.
  • Giulia Baldini
    Institute for Artificial Intelligence in Medicine, University Medicine Essen, Essen, Germany.
  • Cynthia Sabrina Schmidt
    Institute for Artificial Intelligence in Medicine, University Medicine Essen, Essen, Germany.
  • Johannes Haubold
    Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany. Johannes.haubold@uk-essen.de.
  • Felix Nensa
    Institute for AI in Medicine (IKIM), University Hospital Essen, 45131 Essen, Germany.
  • Obioma Pelka
    Department of Computer Science, University of Applied Sciences and Arts Dortmund (FHDO), Dortmund, NRW Germany.
  • René Hosch
    Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Germany.

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