Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs: A systematic review and meta-analysis.

Journal: Computer methods and programs in biomedicine
Published Date: Jan 16, 2020

Abstract

BACKGROUND: Diabetic retinopathy (DR) is one of the leading causes of blindness globally. Earlier detection and timely treatment of DR are desirable to reduce the incidence and progression of vision loss. Currently, deep learning (DL) approaches have offered better performance in detecting DR from retinal fundus images. We, therefore, performed a systematic review with a meta-analysis of relevant studies to quantify the performance of DL algorithms for detecting DR.

Authors

  • Md Mohaimenul Islam
    Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology(ICHIT), Taipei Medical University, Taipei, Taiwan.
  • Hsuan-Chia Yang
    Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan.
  • Tahmina Nasrin Poly
    Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology(ICHIT), Taipei Medical University, Taipei, Taiwan.
  • Wen-Shan Jian
    School of Health Care Administration, Taipei Medical University, Taipei, Taiwan. Electronic address: jj@tmu.edu.tw.
  • Yu-Chuan Jack Li
    Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology(ICHIT), Taipei Medical University, Taipei, Taiwan; Department of Dermatology, Wan Fang Hospital, Taipei, Taiwan. Electronic address: jack@tmu.edu.tw.

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