Performances of Machine Learning in Detecting Glaucoma Using Fundus and Retinal Optical Coherence Tomography Images: A Meta-Analysis

PURPOSE: To evaluate the performance of machine learning (ML) in detecting glaucoma using fundus and retinal optical coherence tomography (OCT) images. DESIGN: Meta-analysis. METHODS: PubMed and EMBASE were searched on August 11, 2021. A bivariate random-effects model was used to pool ML's diagnostic sensitivity, specificity, and area under the curve (AUC). Subgroup analyses were performed based on ML classifier categories and dataset types. RESULTS: One hundred and five studies (3.3%) were retrieved. Seventy-three (69.5%), 30 (28.6%), and 2 (1.9%) studies tested ML using fundus, OCT, and both image types, respectively. Total testing data numbers were 197,174 for fundus and 16,039 for OCT. Overall, ML showed excellent performances for both fundus (pooled sensitivity = 0.92 [95% CI, 0.91-0.93]; speci-ficity = 0.93 [95% CI, 0.91-0.94]; and AUC = 0.97 [95% CI, 0.95-0.98]) and OCT (pooled sensitiv-ity = 0.90 [95% CI, 0.86-0.92]; specificity = 0.91 [95% CI, 0.89-0.92]; and AUC = 0.96 [95% CI, 0.930.97]). ML performed similarly using all data and ex-ternal data for fundus and the external test result of OCT was less robust (AUC = 0.87). When comparing different classifier categories, although support vec-tor machine showed the highest performance (pooled sen-sitivity, specificity, and AUC ranges, 0.92-0.96, 0.950.97, and 0.96-0.99, respectively), results by neural net-work and others were still good (pooled sensitivity, specificity, and AUC ranges, 0.88-0.93, 0.90-0.93, 0.95-0.97, respectively). When analyzed based on dataset types, ML demonstrated consistent performances on clinical datasets (fundus AUC = 0.98 [95% CI, 0.97-0.99] and OCT AUC = 0.95 [95% 0.93-0.97]). CONCLUSIONS: Performance of ML in detecting glau-coma compares favorably to that of experts and is promis-ing for clinical application. Future prospective studies are needed to better evaluate its real-world utility. (C) 2021 Elsevier Inc. All rights reserved.