Hybrid Deep Learning on Single Wide-field Optical Coherence tomography Scans Accurately Classifies Glaucoma Suspects.

Journal: Journal of glaucoma
Published Date: Dec 1, 2017

Abstract

PURPOSE: Existing summary statistics based upon optical coherence tomographic (OCT) scans and/or visual fields (VFs) are suboptimal for distinguishing between healthy and glaucomatous eyes in the clinic. This study evaluates the extent to which a hybrid deep learning method (HDLM), combined with a single wide-field OCT protocol, can distinguish eyes previously classified as either healthy suspects or mild glaucoma.

Authors

  • Hassan Muhammad
    Department of Physiology, Biophysics, and Systems Biology, Weill Cornell Medicine.
  • Thomas J Fuchs
    Weill Cornell Medicine, New York, USA; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA; Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, USA. Electronic address: gac2010@med.cornell.edu.
  • Nicole De Cuir
    Departments of Psychology.
  • Carlos G De Moraes
    Ophthalmology.
  • Dana M Blumberg
    Ophthalmology.
  • Jeffrey M Liebmann
    Bernard and Shirlee Brown Glaucoma Research Laboratory, Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, New York.
  • Robert Ritch
    Einhorn Clinical Research Center, New York Eye and Ear Infirmary of Mount Sinai, New York, New York.
  • Donald C Hood
    Departments of Psychology.

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