Deep Learning for Accurate Diagnosis of Glaucomatous Optic Neuropathy Using Digital Fundus Image: A Meta-Analysis

Mohaimenul Islam, Tahmina Nasrin Poly, Hsuan Chia Yang, Suleman Atique, Yu Chuan Jack Li

Research output: Contribution to journalArticle

Abstract

We conducted a study to evaluate the algorithms based on deep learning to automatically diagnosis of GON from digital fundus images. A systematic articles search was conducted in PubMed, EMBASE, Google Scholar for the study that investigated the performance of deep learning algorithms for the detection of GON. A total of eight studies were included in this study, of which 5 studies were used to conduct our meta-analysis. The pooled AUROC for detecting GON was 0.98. However, the sensitivity and specificity of deep learning to detect GON were 0.90 (95% CI: 0.90-0.91), and 0.94 (95%CI: 0.93-0.94), respectively.

Original languageEnglish
Pages (from-to)153-157
Number of pages5
JournalStudies in Health Technology and Informatics
Volume270
DOIs
Publication statusPublished - Jun 16 2020

Keywords

  • artificial intelligence
  • deep learning
  • fundus image
  • Glaucoma
  • glaucomatous optic neuropathy

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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