Artificial Intelligence in Anterior Chamber Evaluation: A Systematic Review and Meta-Analysis

Abstract

Précis: In this meta-analysis of 6 studies and 5269 patients, deep learning algorithms applied to AS-OCT demonstrated excellent diagnostic performance for closed angle compared with gonioscopy, with a pooled sensitivity and specificity of 94% and 93.6%, respectively. Purpose: This study aimed to review the literature and compare the accuracy of deep learning algorithms (DLA) applied to anterior segment optical coherence tomography images (AS-OCT) against gonioscopy in detecting angle closure in patients with glaucoma. Methods: We performed a systematic review and meta-analysis evaluating DLA in AS-OCT images for the diagnosis of angle closure compared with gonioscopic evaluation. PubMed, Scopus, Embase, Lilacs, Scielo, and Cochrane Central Register of Controlled Trials were searched. The bivariate model was used to calculate pooled sensitivity and specificity. Results: The initial search identified 214 studies, of which 6 were included for final analysis. The total study population included 5269 patients. The combined sensitivity of the DLA compared with gonioscopy was 94.0% (95% CI: 83.8%–97.9%), whereas the pooled specificity was 93.6% (95% CI: 85.7%–97.3%). Sensitivity analyses removing each individual study showed a pooled sensitivity in the range of 90.1%–95.1%. Similarly, specificity results ranged from 90.3% to 94.5% with the removal of each individual study and recalculation of pooled specificity. Conclusion: DLA applied to AS-OCT has excellent sensitivity and specificity in the identification of angle closure. This technology may be a valuable resource in the screening of populations without access to experienced ophthalmologists who perform gonioscopy.