Deep Learning in Glaucoma Detection and Progression Prediction: A Systematic Review and Meta-Analysis.

ABSTRACT: Purpose: To evaluate the performance of deep learning (DL) in diagnosing glaucoma and predicting its progression using fundus photography and retinal optical coherence tomography (OCT) images. Materials and Methods: Relevant studies published up to 30 October 2024 were retrieved from PubMed, Medline, EMBASE, Cochrane Library, Web of Science, and ClinicalKey. A bivariate random-effects model was employed to calculate pooled sensitivity, specificity, positive and negative likelihood ratios, and area under the receiver operating characteristic curve (AUROC). Results: A total of 48 studies were included in the meta-analysis. DL algorithms demonstrated high diagnostic performance in glaucoma detection using fundus photography and OCT images. For fundus photography, the pool

SUBMITTER: Ling XC 

PROVIDER: S-EPMC11852503 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature