Artificial intelligence to predict clinical disability in patients with multiple sclerosis using FLAIR MRI.

Journal: Diagnostic and interventional imaging
Published Date:

Abstract

PURPOSE: The purpose of this study was to create an algorithm that combines multiple machine-learning techniques to predict the expanded disability status scale (EDSS) score of patients with multiple sclerosis at two years solely based on age, sex and fluid attenuated inversion recovery (FLAIR) MRI data.

Authors

  • P Roca
    Pixyl, Research and Development Laboratory, 38000 Grenoble, France. Electronic address: contact@pixyl.ai.
  • A Attye
    Grenoble Alpes University, 38000 Grenoble, France; Sydney Imaging Lab, Sydney University, 2006 Sydney, NSW, Australia.
  • L Colas
    Imaging Department, Lille Catholic Hospitals, Lille Catholic University, 59000 Lille, France.
  • A Tucholka
    Pixyl, Research and Development Laboratory, 38000 Grenoble, France.
  • P Rubini
    Pixyl, Research and Development Laboratory, 38000 Grenoble, France.
  • S Cackowski
    University Grenoble Alpes, Inserm, U1216, Grenoble Institute Neurosciences, 38000 Grenoble, France.
  • J Ding
    Imaging Department, Lille Catholic Hospitals, Lille Catholic University, 59000 Lille, France.
  • J-F Budzik
    Service d'Imagerie Musculosquelettique GHICL, Université Catholique de Lille, Hôpital Saint-Vincent de Paul, 59000 Lille, France.
  • F Renard
    University Grenoble Alpes, CNRS, Grenoble INP, LIG, 38000 Grenoble, France; University Grenoble Alpes, AGEIS, 38000 Grenoble, France.
  • S Doyle
    Pixyl, Research and Development Laboratory, 38000 Grenoble, France.
  • E L Barbier
    University Grenoble Alpes, Inserm, U1216, Grenoble Institute Neurosciences, 38000 Grenoble, France.
  • I Bousaid
    Direction de la Transformation Numérique et des Systèmes d'Information, Gustave Roussy, 94800 Villejuif, France.
  • R Casey
    Department of Neurology-Multiple Sclerosis, Pathologies de la myéline et neuro-inflammation, Hôpital Pierre Wertheimer, Hospices Civils de Lyon, 69500 Bron, France; Université Claude Bernard Lyon 1, Université de Lyon, 69622 Villeurbanne, France; Observatoire Français de la Sclérose en Plaques, Centre de Recherche en Neurosciences de Lyon, INSERM 1028 et CNRS UMR 5292, 69003 Lyon, France; Eugène Devic EDMUS Foundation Against Multiple Sclerosis, 69500 Bron, France.
  • S Vukusic
    Department of Neurology-Multiple Sclerosis, Pathologies de la myéline et neuro-inflammation, Hôpital Pierre Wertheimer, Hospices Civils de Lyon, 69500 Bron, France; Université Claude Bernard Lyon 1, Université de Lyon, 69622 Villeurbanne, France; Observatoire Français de la Sclérose en Plaques, Centre de Recherche en Neurosciences de Lyon, INSERM 1028 et CNRS UMR 5292, 69003 Lyon, France; Eugène Devic EDMUS Foundation Against Multiple Sclerosis, 69500 Bron, France.
  • N Lassau
    Department of Radiology, Gustave Roussy, IR4M, UMR8081, CNRS, Université Paris-Sud, Université Paris-Saclay, 94805 Villejuif, France.
  • S Verclytte
    Imaging Department, Lille Catholic Hospitals, Lille Catholic University, 59000 Lille, France.
  • F Cotton
    Observatoire Français de la Sclérose en Plaques, Centre de Recherche en Neurosciences de Lyon, INSERM 1028 et CNRS UMR 5292, 69003 Lyon, France.