A Deep Learning Systemfor Automatic Assessment of Anterior Chamber Angle in Ultrasound Biomicroscopy Images

Purpose: To develop and assess a deep learning systemthat automatically detects angle closure and quantitatively measures angle parameters from ultrasound biomicroscopy (UBM) images using a deep learning algorithm. Methods: A total of 3788 UBM images (2146 open angle and 1642 angle closure) from 1483 patients were collected. We developed a convolutional neural network (CNN) based on the InceptionV3 network for automatic classification of angle closure and open angle. For nonclosed images, we developed a CNNbased on the EfficienttNetB3 network for the automatic localization of the scleral spur and the angle recess; then, the Unet network was used to segment the anterior chamber angle (ACA) tissue automatically. Based on the results of the latter two processes, we developed an algorithm to automatically measure the trabecular-iris angle ( TIA500 and TIA750), angle- opening distance (AOD500 and AOD750), and angle recess area (ARA500 and ARA750) for quantitative evaluation of angle width. Results: Using manual labeling as the reference standard, the ACA classification network's accuracy reached 98.18%, and the sensitivity and specificity for angle closure reached 98.74% and 97.44%, respectively. The deep learning system realized the automatic measurement of the angle parameters, and the mean of differences was generally small between automatic measurement and manual measurement. The coefficients of variation of TIA500, TIA750, AOD500, AOD750, ARA500, and ARA750 measured by the deep learning system were 5.77%, 4.67%, 10.76%, 7.71%, 16.77%, and 12.70%, respectively. Thewithin-subject standard deviations of TIA500, TIA750, AOD500, AOD750, ARA500, and ARA750 were 5.77 degrees, 4.56 degrees, 155.92 mu m, 147.51 mu m, 0.10 mm(2), and 0.12 mm(2), respectively. The intraclass correlation coefficients of all the angle parameters were greater than 0.935. Conclusions: The deep learning system can effectively and accurately evaluate the ACA automatically based on fully automated analysis of a UBM image. Translational Relevance: The present work suggests that the deep learning system described here could automatically detect angle closure and quantitatively measure angle parameters from UBM images and enhancing the intelligent diagnosis and management of primary angle-closure glaucoma.