Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps.

Journal: Ophthalmology
Published Date: Mar 1, 2020

Abstract

PURPOSE: To develop and evaluate a deep learning system for differentiating between eyes with and without glaucomatous visual field damage (GVFD) and predicting the severity of GFVD from spectral domain OCT (SD OCT) optic nerve head images.

Authors

  • Mark Christopher
    Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California San Diego, La Jolla, California, United States.
  • Christopher Bowd
    Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, California.
  • Akram Belghith
    Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California San Diego, La Jolla, California, United States.
  • Michael H Goldbaum
    Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California San Diego, La Jolla, California, United States.
  • Robert N Weinreb
    Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, California.
  • Massimo A Fazio
    Department of Ophthalmology, University of Alabama at Birmingham, Birmingham, Alabama.
  • Christopher A Girkin
    Department of Ophthalmology, University of Alabama at Birmingham, Birmingham, Alabama.
  • Jeffrey M Liebmann
    Bernard and Shirlee Brown Glaucoma Research Laboratory, Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, New York.
  • Linda M Zangwill
    Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, California.

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