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

研究成果: 雜誌貢獻文章同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
頁(從 - 到)153-157
頁數5
期刊Studies in Health Technology and Informatics
270
DOIs
出版狀態已發佈 - 六月 16 2020

ASJC Scopus subject areas

  • 生物醫學工程
  • 健康資訊學
  • 健康資訊管理

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