Predicting Visual Improvement After Macular Hole Surgery: A Combined Model Using Deep Learning and Clinical Features.

Journal: Translational vision science & technology
Published Date: Apr 1, 2022

Abstract

PURPOSE: The purpose of this study was to assess the feasibility of deep learning (DL) methods to enhance the prediction of visual acuity (VA) improvement after macular hole (MH) surgery from a combined model using DL on high-definition optical coherence tomography (HD-OCT) B-scans and clinical features.

Authors

  • Alexandre Lachance
    Faculté de Médecine, Université Laval, Québec, QC, Canada.
  • Mathieu Godbout
    Département d'informatique et de Génie Logiciel, Université Laval, Québec, QC, Canada.
  • Fares Antaki
    Department of Ophthalmology, University of Montreal, Montreal, QC H3T 1J4, Canada.
  • Mélanie Hébert
    Faculté de Médecine, Université Laval, Québec, QC, Canada.
  • Serge Bourgault
    Faculté de Médecine, Université Laval, Québec, QC, Canada.
  • Mathieu Caissie
    Faculté de Médecine, Université Laval, Québec, QC, Canada.
  • Éric Tourville
    Faculté de Médecine, Université Laval, Québec, QC, Canada.
  • Audrey Durand
    Department of Computer Science and Software Engineering and Department of Electrical and Computer Engineering, Université Laval, Québec City, Québec, Canada.
  • Ali Dirani
    Faculté de Médecine, Université Laval, Québec, QC, Canada.

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