A Deep Learning Approach for Automated Detection of Geographic Atrophy from Color Fundus Photographs.

Journal: Ophthalmology
Published Date:

Abstract

PURPOSE: To assess the utility of deep learning in the detection of geographic atrophy (GA) from color fundus photographs and to explore potential utility in detecting central GA (CGA).

Authors

  • Tiarnan D Keenan
    National Eye Institute, National Institutes of Health, Bethesda, Maryland.
  • Shazia Dharssi
    National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland; National Eye Institute, National Institutes of Health, Bethesda, Maryland.
  • Yifan Peng
    Department of Population Health Sciences, Weill Cornell Medicine, New York, USA.
  • Qingyu Chen
    Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA.
  • Elvira Agrón
    National Eye Institute, National Institutes of Health, Bethesda, Maryland.
  • Wai T Wong
    National Eye Institute, National Institutes of Health, Bethesda, Maryland.
  • Zhiyong Lu
    National Center for Biotechnology Information, Bethesda, MD 20894 USA.
  • Emily Y Chew
    National Eye Institute, National Institutes of Health, Bethesda, Maryland. Electronic address: echew@nei.nih.gov.