Deep Learning Classification of Angle Closure based on Anterior Segment OCT

Purpose: To assess the performance and generalizability of a convolutional neural network (CNN) model for objective and high -throughput identification of primary angle -closure disease (PACD) as well as PACD stage differentiation on anterior segment swept -source OCT (AS-OCT). Design: Cross-sectional. Participants: Patients from 3 different eye centers across China and Singapore were recruited for this study. Eight hundred forty-one eyes from the 2 Chinese centers were divided into 170 control eyes, 488 PACS, and 183 PAC + PACG eyes. An additional 300 eyes were recruited from Singapore National Eye Center as a testing data set, divided into 100 control eyes, 100 PACS, and 100 PAC + PACG eyes. Methods: Each participant underwent standardized ophthalmic examination and was classified by the presiding physician as either control, primary angle -closure suspect (PACS), primary angle closure (PAC), or primary angle -closure glaucoma (PACG). Deep Learning model was used to train 3 different CNN classifiers: classifier 1 aimed to separate control versus PACS versus PAC + PACG; classifier 2 aimed to separate control versus PACD; and classifier 3 aimed to separate PACS versus PAC + PACG. All classifiers were evaluated on independent validation sets from the same region, China and further tested using data from a different country, Singapore. Main Outcome Measures: Area under receiver operator characteristic curve (AUC), precision, and recall. Results: Classifier 1 achieved an AUC of 0.96 on validation set from the same region, but dropped to an AUC of 0.84 on test set from a different country. Classifier 2 achieved the most generalizable performance with an AUC of 0.96 on validation set and AUC of 0.95 on test set. Classifier 3 showed the poorest performance, with an AUC of 0.83 and 0.64 on test and validation data sets, respectively. Conclusions: Convolutional neural network classifiers can effectively distinguish PACD from controls on AS-OCT with good generalizability across different patient cohorts. However, their performance is moderate when trying to distinguish PACS versus PAC + PACG. Financial Disclosures: The authors have no proprietary or commercial interest in any materials discussed in this article. (c) 2023 Published by Elsevier Inc. on behalf of the American Academy of Ophthalmology