A deep learning model for the diagnosis of sacroiliitis according to Assessment of SpondyloArthritis International Society classification criteria with magnetic resonance imaging.

Journal: Diagnostic and interventional imaging
Published Date: Jan 1, 2023

Abstract

PURPOSE: The purpose of this study was to develop and evaluate a deep learning model to detect bone marrow edema (BME) in sacroiliac joints and predict the MRI Assessment of SpondyloArthritis International Society (ASAS) definition of active sacroiliitis in patients with chronic inflammatory back pain.

Authors

  • Adrien Bordner
    Sorbonne Médecine Université, 75013 Paris, France; Department of Radiology, Hôpital Cochin, APHP, 75014 Paris, France. Electronic address: adrien.bordner@aphp.fr.
  • Theodore Aouad
    Université Paris-Saclay, CentraleSupélec, Inria, Centre for Visual Computing, 91190, Gif-sur-Yvette, France.
  • Clementina Lopez Medina
    Department of Rheumatology, Reina Sofia University Hospital, IMIBIC, University of Cordoba, 14004 Cordoba, Spain.
  • Sisi Yang
    Department of Radiology, Hôpital Cochin, APHP, 75014 Paris, France; Université Paris Cité, 75006 Paris, France.
  • Anna Molto
    Cochin Hospital, AP-HP, INSERM U1153, PRES Sorbonne Paris-Cité, Paris Descartes University, Paris, France.
  • Hugues Talbot
    OPIS - Optimisation Imagerie et Santé, Université Paris-Saclay, Inria, CentraleSupélec, CVN - Centre de vision numérique, 91190 Gif-Sur-Yvette, France.
  • Maxime Dougados
    Université Paris Cité, 75006 Paris, France; Department of Rheumatology, Hôpital Cochin, APHP, 75014 Paris, France; INSERM U1153, Clinical Epidemiology and Biostatistics, PRES Sorbonne Paris-Cité, 75004 Paris, France.
  • Antoine Feydy
    Department of Radiology B, Cochin Hospital, AP-HP, 75014 Paris, France; Université de Paris, 75006 Paris, France.

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