Automatic Anterior Chamber Angle Classification Using Deep Learning System and Anterior Segment Optical Coherence Tomography Images.

Purpose: The purpose of this study was to develop a software package for the automatic classification of anterior chamber angle using anterior segment optical coherence tomography (AS-OCT). Methods: AS-OCT images were collected from subjects with open, narrow, and closure anterior chamber angles, which were graded based on ultrasound biomicroscopy (UBM) results. The Inception version 3 network and the transfer learning technique were applied in the design of an algorithm for anterior chamber angle classification. The classification performance was evaluated by fivefold cross-validation and on an independent test dataset. Results: The proposed algorithm reached a sensitivity of 0.999 and specificity of 1.000 in the judgment of closed and nonclosed angles. The overall classification of the proposed method in open angle, narrow angle, and angle-closure classifications reached a sensitivity of 0.989 and specificity of 0.995. Additionally, the sensitivity and specificity reached 1.000 and 1.000 for angle-closure, 0.983 and 0.993 for narrow angle, and 0.985 and 0.991 for open angle. Conclusions: The experimental results showed that the proposed method can achieve a high accuracy of anterior chamber angle classification using AS-OCT images, and could be of value in future practice. Translational Relevance: The proposed deep learning-based method that automate the classification of anterior chamber angle can facilitate clinical assessment of glaucoma.