A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images

Abstract Purpose Anterior segment optical coherence tomography (AS-OCT) provides an objective imaging modality for visually identifying anterior segment structures. An automated detection system could assist ophthalmologists in interpreting AS-OCT images for presence of angle closure. Design Development of an artificial intelligence automated detection system for the presence of angle closure. Methods A deep learning system for automated angle-closure detection in AS-OCT images was developed, and this was compared with another automated angle-closure detection system based on quantitative features. A total of 4135 Visante AS-OCT images from 2113 subjects (8270 anterior chamber angle (ACA) images with 7375 open-angle and 895 angle-closure) were examined. The deep learning angle-closure detection system for a two-class classification problem was tested by five-fold cross-validation. The deep learning system, and the automated angle-closure detection system based on quantitative features were evaluated against clinician’s grading of AS-OCT images as the reference standard. Results The area under the receiver operating characteristic curve (AUC) of the system using quantitative features was 0.90 (95% confidence interval (CI), 0.891-0.914) with a sensitivity of 0.79 ± 0.037 and a specificity of 0.87 ± 0.009, while the AUC of the deep learning system was 0.96 (95% CI, 0.953-0.968) with a sensitivity of 0.90 ± 0.02 and a specificity of 0.92 ± 0.008, against clinician’s grading of AS-OCT images as the reference standard. Conclusions The results demonstrate the potential of the deep learning system for angle-closure detection in AS-OCT images.