Deep Learning Automated Detection of Reticular Pseudodrusen from Fundus Autofluorescence Images or Color Fundus Photographs in AREDS2.

Abstract

Authors

• Tiarnan D L Keenan
  Division of Epidemiology and Clinical Applications, 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.
• Yifan Peng
  Department of Population Health Sciences, Weill Cornell Medicine, New York, USA.
• Amitha Domalpally
  Department of Ophthalmology and Visual Sciences, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America.
• Elvira Agrón
  National Eye Institute, National Institutes of Health, Bethesda, Maryland.
• Christopher K Hwang
  Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.
• Alisa T Thavikulwat
  Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.
• Debora H Lee
  Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.
• Daniel Li
  National Center for Biotechnology Information, National Library of Medicine, 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.

Keywords

External Resources