Automatic classification of anterior chamber angle using ultrasound biomicroscopy and deep learning

Abstract

Purpose: To develop a software package for automated classification of anterior chamber angle of the eye by using ultrasound biomicroscopy. Methods: Ultrasound biomicroscopy images were collected, and the trabecular-iris angle was manually measured and classified into three categories: open angle, narrow angle, and angle closure. Inception v3 was used as the classifying convolutional neural network and the algorithm was trained. Results: With a recall rate of 97% in the test set, the neural network’s classification accuracy can reach 97.2% and the overall area under the curve was 0.988. The sensitivity and specificity were 98.04% and 99.09% for the open angle, 96.30% and 98.13% for the narrow angle, and 98.21% and 99.05% for the angle closure categories, respectively. Conclusions: Preliminary results show that an automated classification of the anterior chamber angle achieved satisfying sensitivity and specificity and could be helpful in clinical practice. Translational Relevance: The present work suggests that the algorithm described here could be useful in the categorizing of anterior chamber angle and screening for subjects who are at high risk of angle closure.