Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study.

Journal: PloS one
PMID: 35157713

Abstract

PURPOSE: To evaluate the predictive ability of a deep learning-based algorithm to determine long-term best-corrected distance visual acuity (BCVA) outcomes in neovascular age-related macular degeneration (nARMD) patients using baseline swept-source optical coherence tomography (SS-OCT) and OCT-angiography (OCT-A) data.

Authors

  • Simrat K Sodhi
    School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom.
  • Austin Pereira
    Department of Ophthalmology & Visual Sciences, University of Toronto, Toronto, ON Canada.
  • Jonathan D Oakley
    Voxeleron LLC, Pleasanton, California, United States.
  • John Golding
    Vitreous Retina Macula Specialists of Toronto, Etobicoke, ON, Canada.
  • Carmelina Trimboli
    Vitreous Retina Macula Specialists of Toronto, Etobicoke, ON, Canada.
  • Daniel B Russakoff
    Voxeleron LLC, Pleasanton, California, United States.
  • Netan Choudhry
    Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.

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