Performance analysis of a deep-learning algorithm to detect the presence of inflammation in MRI of sacroiliac joints in patients with axial spondyloarthritis.

Journal: Annals of the rheumatic diseases
Published Date: Jan 2, 2025

Abstract

OBJECTIVES: To assess the ability of a previously trained deep-learning algorithm to identify the presence of inflammation on MRI of sacroiliac joints (SIJ) in a large external validation set of patients with axial spondyloarthritis (axSpA).

Authors

  • Joeri Nicolaes
    Department of Electrical Engineering (ESAT), Center for Processing Speech and Images, KU Leuven, Leuven, Belgium; UCB, Brussels, Belgium. Electronic address: joeri.nicolaes@ucb.com.
  • Evi Tselenti
    UCB, Slough, UK.
  • Theodore Aouad
    Université Paris-Saclay, CentraleSupélec, Inria, Centre for Visual Computing, 91190, Gif-sur-Yvette, France.
  • Clementina López-Medina
    Rheumatology Department, Reina Sofia Hospital, Cordoba / IMIBIC / University of Cordoba, Cordoba, Spain.
  • Antoine Feydy
    Department of Radiology B, Cochin Hospital, AP-HP, 75014 Paris, France; Université de Paris, 75006 Paris, France.
  • Hugues Talbot
    OPIS - Optimisation Imagerie et Santé, Université Paris-Saclay, Inria, CentraleSupélec, CVN - Centre de vision numérique, 91190 Gif-Sur-Yvette, France.
  • Bengt Hoepken
    UCB, Monheim am Rhein, Germany.
  • Natasha de Peyrecave
    UCB, Brussels, Belgium.
  • 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.

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