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Abstract #89918 Published in IGR 21-3
Deep Learning for Accurate Diagnosis of Glaucomatous Optic Neuropathy Using Digital Fundus Image: A Meta-Analysis
Islam M; Poly TN; Yang HC; Atique S; Li YJStudies in health technology and informatics 2020; 270: 153-157
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.
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Classification:
6.30 Other (Part of: 6 Clinical examination methods)
6.8.2 Posterior segment (Part of: 6 Clinical examination methods > 6.8 Photography)
2.14 Optic disc (Part of: 2 Anatomical structures in glaucoma)
