Deep Learning for Accurate Diagnosis of Glaucomatous Optic Neuropathy Using Digital Fundus Image: A Meta-Analysis
Islam Mohaimenul, Poly Tahmina Nasrin, Yang Hsuan Chia, Atique Suleman, Yu‐Chuan Li
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.
Topics & Concepts
Fundus (uterus)Artificial intelligenceMeta-analysisDeep learningMedicineOphthalmologyComputer scienceOptometryMachine learningPathologyRetinal Imaging and AnalysisGlaucoma and retinal disordersRetinal and Optic Conditions