Accuracy of automated machine learning in classifying retinal pathologies from ultra-widefield pseudocolour fundus images.

Journal: The British journal of ophthalmology
Published Date: Aug 3, 2021

Abstract

AIMS: Automated machine learning (AutoML) is a novel tool in artificial intelligence (AI). This study assessed the discriminative performance of AutoML in differentiating retinal vein occlusion (RVO), retinitis pigmentosa (RP) and retinal detachment (RD) from normal fundi using ultra-widefield (UWF) pseudocolour fundus images.

Authors

  • Fares Antaki
    Department of Ophthalmology, University of Montreal, Montreal, QC H3T 1J4, Canada.
  • Razek Georges Coussa
    Department of Ophthalmology and Visual Sciences, Carver College of Medicine, University of Iowa, Iowa City, IA, USA.
  • Ghofril Kahwati
    Institut National des Sciences Appliquées de Toulouse (INSA Toulouse), Toulouse, France.
  • Karim Hammamji
    Department of Ophthalmology, Université de Montréal, Montreal, Québec, Canada.
  • Mikael Sebag
    Department of Ophthalmology, Université de Montréal, Montreal, QC, Canada. sebag.mikael@gmail.com.
  • Renaud Duval
    Department of Ophthalmology, University of Montreal, Montreal, QC H3T 1J4, Canada.

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