The Role of Artificial Intelligence in Predicting Optic Neuritis Subtypes From Ocular Fundus Photographs.

Journal: Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society
Published Date:

Abstract

BACKGROUND: Optic neuritis (ON) is a complex clinical syndrome that has diverse etiologies and treatments based on its subtypes. Notably, ON associated with multiple sclerosis (MS ON) has a good prognosis for recovery irrespective of treatment, whereas ON associated with other conditions including neuromyelitis optica spectrum disorders or myelin oligodendrocyte glycoprotein antibody-associated disease is often associated with less favorable outcomes. Delay in treatment of these non-MS ON subtypes can lead to irreversible vision loss. It is important to distinguish MS ON from other ON subtypes early, to guide appropriate management. Yet, identifying ON and differentiating subtypes can be challenging as MRI and serological antibody test results are not always readily available in the acute setting. The purpose of this study is to develop a deep learning artificial intelligence (AI) algorithm to predict subtype based on fundus photographs, to aid the diagnostic evaluation of patients with suspected ON.

Authors

  • Étienne Bénard-Séguin
    Division of Ophthalmology (EB-S, AS, AA-A, AS-B, DW, SS, FC), Department of Surgery, University of Calgary, Calgary, Canada; Department of Biomedical Engineering (CN), University of Calgary, Calgary, Canada; Departments of Neurology (LBDL) and Ophthalmology (LBDL), University of Michigan, Ann Arbor, Michigan; and Department of Clinical Neurosciences (SS, FC), University of Calgary, Calgary, Canada.
  • Christopher Nielsen
  • Abdullah Sarhan
  • Abdullah Al-Ani
  • Antoine Sylvestre-Bouchard
  • Derek M Waldner
  • Lindsey B De Lott
  • Suresh Subramaniam
  • Fiona Costello