Deep Learning Classifiers for Automated Detection of Gonioscopic Angle Closure Based on Anterior Segment OCT Images.

Journal: American journal of ophthalmology
PMID:

Abstract

PURPOSE: To develop and test deep learning classifiers that detect gonioscopic angle closure and primary angle closure disease (PACD) based on fully automated analysis of anterior segment OCT (AS-OCT) images.

Authors

  • Benjamin Y Xu
    Roski Eye Institute, Department of Ophthalmology, Keck School of Medicine at the University of Southern California, Los Angeles, California, USA. Electronic address: benjamin.xu@med.usc.edu.
  • Michael Chiang
    Sol Price School of Public Policy, University of Southern California, Los Angeles, California, USA.
  • Shreyasi Chaudhary
    Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA.
  • Shraddha Kulkarni
    Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA.
  • Anmol A Pardeshi
    Roski Eye Institute, Department of Ophthalmology, Keck School of Medicine at the University of Southern California, Los Angeles, California, USA.
  • Rohit Varma
    University of Southern California Gayle and Edward Roski Eye Institute, Los Angeles, California.