Automated Fundus Image Quality Assessment in Retinopathy of Prematurity Using Deep Convolutional Neural Networks.

Journal: Ophthalmology. Retina
PMID: 31044738

Abstract

PURPOSE: Accurate image-based ophthalmic diagnosis relies on fundus image clarity. This has important implications for the quality of ophthalmic diagnoses and for emerging methods such as telemedicine and computer-based image analysis. The purpose of this study was to implement a deep convolutional neural network (CNN) for automated assessment of fundus image quality in retinopathy of prematurity (ROP).

Authors

  • Aaron S Coyner
    Department of Ophthalmology, Oregon Health and Science University, Portland, Oregon.
  • Ryan Swan
    Medical Informatics & Clinical Epidemiology, and.
  • J Peter Campbell
    Department of Ophthalmology, Oregon Health and Science University, Portland, Oregon.
  • Susan Ostmo
    Department of Ophthalmology, Oregon Health and Science University, Portland, Oregon.
  • James M Brown
    Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, MGH/Harvard Medical School, Charlestown, MA, United States.
  • Jayashree Kalpathy-Cramer
    Department of Radiology, MGH/Harvard Medical School, Charlestown, Massachusetts.
  • Sang Jin Kim
    Ophthalmology Oregon Health & Science University, Portland, OR, United States.
  • Karyn E Jonas
    Ophthalmology, University of Illinois at Chicago, Chicago, IL, United States.
  • R V Paul Chan
    Ophthalmology, Illinois Eye and Ear Infirmary, Chicago, IL, United States.
  • Michael F Chiang
    National Eye Institute, National Institutes of Health, Bethesda, Maryland.

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