Deep-Learning-Based Pre-Diagnosis Assessment Module for Retinal Photographs: A Multicenter Study.

Journal: Translational vision science & technology
Published Date: Sep 1, 2021

Abstract

PURPOSE: Artificial intelligence (AI) deep learning (DL) has been shown to have significant potential for eye disease detection and screening on retinal photographs in different clinical settings, particular in primary care. However, an automated pre-diagnosis image assessment is essential to streamline the application of the developed AI-DL algorithms. In this study, we developed and validated a DL-based pre-diagnosis assessment module for retinal photographs, targeting image quality (gradable vs. ungradable), field of view (macula-centered vs. optic-disc-centered), and laterality of the eye (right vs. left).

Authors

  • Vincent Yuen
    Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong.
  • Anran Ran
    Department of Ophthalmology and Visual Sciences, the Chinese University of Hong Kong, Hong Kong SAR.
  • Jian Shi
  • Kaiser Sham
    Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong.
  • Dawei Yang
    Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital Fudan University, Shanghai, China.
  • Victor T T Chan
    Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong.
  • Raymond Chan
    Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong.
  • Jason C Yam
    Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China.
  • Clement C Tham
    Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Hong Kong Eye Hospital, Hong Kong Special Administrative Region, China; Prince of Wales Hospital, Hong Kong Special Administrative Region, China.
  • Gareth J McKay
    Center for Public Health, Royal Victoria Hospital, Queen's University Belfast, Belfast, UK.
  • Michael A Williams
    Center for Medical Education, Royal Victoria Hospital, Queen's University Belfast, Belfast, UK.
  • Leopold Schmetterer
    Singapore Eye Research Institute, Singapore National Eye Center, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore.
  • Ching-Yu Cheng
    Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore.
  • Vincent Mok
    Division of Neurology Department of Medicine and Therapeutics Faculty of Medicine The Chinese University of Hong Kong Shatin NT Hong Kong.
  • Christopher L Chen
    Memory, Aging and Cognition Center, Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
  • Tien Y Wong
    Singapore National Eye Center, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore.
  • Carol Y Cheung
    Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China. Electronic address: carolcheung@cuhk.edu.hk.

Keywords

External Resources

Popular Topics
Recent Journals