DeepSeeNet: A Deep Learning Model for Automated Classification of Patient-based Age-related Macular Degeneration Severity from Color Fundus Photographs.

Journal: Ophthalmology
Published Date: Nov 22, 2018

Abstract

PURPOSE: In assessing the severity of age-related macular degeneration (AMD), the Age-Related Eye Disease Study (AREDS) Simplified Severity Scale predicts the risk of progression to late AMD. However, its manual use requires the time-consuming participation of expert practitioners. Although several automated deep learning systems have been developed for classifying color fundus photographs (CFP) of individual eyes by AREDS severity score, none to date has used a patient-based scoring system that uses images from both eyes to assign a severity score.

Authors

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

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