Code-Free Deep Learning Glaucoma Detection on Color Fundus Images.

Journal: Ophthalmology science
Published Date: Jan 30, 2025

Abstract

OBJECTIVE: Code-free deep learning (CFDL) allows clinicians with no coding experience to build their own artificial intelligence models. This study assesses the performance of CFDL in glaucoma detection from fundus images in comparison to expert-designed models.

Authors

  • Daniel Milad
    Faculty of Medicine, University of Montreal, Montreal, QC, Canada; Department of Ophthalmology, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada.
  • Fares Antaki
    Department of Ophthalmology, University of Montreal, Montreal, QC H3T 1J4, Canada.
  • David Mikhail
    Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
  • Andrew Farah
    Faculty of Medicine, McGill University, Montreal, QC H3A 0G4, Canada.
  • Jonathan El-Khoury
    Department of Ophthalmology, University of Montreal, Montreal, QC H3T 1J4, Canada.
  • Samir Touma
    Department of Ophthalmology, Université de Montréal, Montréal, QC, Canada.
  • Georges M Durr
    Department of Ophthalmology, Université de Montréal, Montreal, Québec, Canada.
  • Taylor Nayman
    Department of Ophthalmology, University of Montreal, Montreal, QC H3T 1J4, Canada.
  • Clement Playout
  • Pearse A Keane
    National Institute for Health Research Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK.
  • Renaud Duval
    Department of Ophthalmology, University of Montreal, Montreal, QC H3T 1J4, Canada.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals