Performance of deep neural network-based artificial intelligence method in diabetic retinopathy screening: a systematic review and meta-analysis of diagnostic test accuracy.

Journal: European journal of endocrinology
Published Date: Jun 1, 2020

Abstract

OBJECTIVE: Automatic diabetic retinopathy screening system based on neural networks has been used to detect diabetic retinopathy (DR). However, there is no quantitative synthesis of performance of these methods. We aimed to estimate the sensitivity and specificity of neural networks in DR grading.

Authors

  • Shirui Wang
    Eight-year Program of Clinical Medicine, Peking Union Medical College Hospital (PUMCH), Chinese Academe of Medical Sciences & Peking Union Medical College (CAMS & PUMC), Beijing, China.
  • Yuelun Zhang
    Medical Research Center, PUMCH, CAMS & PUMC, Beijing, China.
  • Shubin Lei
    Eight-year Program of Clinical Medicine, Peking Union Medical College Hospital (PUMCH), Chinese Academe of Medical Sciences & Peking Union Medical College (CAMS & PUMC), Beijing, China.
  • Huijuan Zhu
    Department of Endocrinology, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, Beijing, China.
  • Jianqiang Li
    School of Software Engineering, Beijing University of Technology, Beijing, China. Electronic address: lijianqiang@bjut.edu.cn.
  • Qing Wang
    School of Chemistry and Chemical Engineering, Southwest Petroleum University, Chengdu 610500, China. qwang@163.com.
  • Jijiang Yang
    Research Institute of Information and Technology, Tsinghua University, Beijing, China.
  • Shi Chen
    Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, PUMCH, CAMS & PUMC, Beijing, China.
  • Hui Pan
    Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, PUMCH, CAMS & PUMC, Beijing, China.

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