Deep Learning Classifiers for Automated Detection of Gonioscopic Angle Closure Based on Anterior Segment OCT Images

Abstract

Purpose: To develop and test deep learning classifiers that detect gonioscopic angle closure and primary angle closure disease (PACD) based on fully automated analysis of anterior segment OCT (AS-OCT) images.

Methods: Subjects were recruited as part of the Chinese-American Eye Study (CHES), a population-based study of Chinese Americans in Los Angeles, California, USA. Each subject underwent a complete ocular examination including gonioscopy and AS-OCT imaging in each quadrant of the anterior chamber angle (ACA). Deep learning methods were used to develop 3 competing multi-class convolutional neural network (CNN) classifiers for modified Shaffer grades 0, 1, 2, 3, and 4. Binary probabilities for closed (grades 0 and 1) and open (grades 2, 3, and 4) angles were calculated by summing over the corresponding grades. Classifier performance was evaluated by 5-fold cross-validation and on an independent test dataset. Outcome measures included area under the receiver operating characteristic curve (AUC) for detecting gonioscopic angle closure and PACD, defined as either 2 or 3 quadrants of gonioscopic angle closure per eye.

Results: A total of 4036 AS-OCT images with corresponding gonioscopy grades (1943 open, 2093 closed) were obtained from 791 CHES subjects. Three competing CNN classifiers were developed with a cross-validation dataset of 3396 images (1632 open, 1764 closed) from 664 subjects. The remaining 640 images (311 open, 329 closed) from 127 subjects were segregated into a test dataset. The best-performing classifier was developed by applying transfer learning to the ResNet-18 architecture. For detecting gonioscopic angle closure, this classifier achieved an AUC of 0.933 (95% confidence interval, 0.925-0.941) on the cross-validation dataset and 0.928 on the test dataset. For detecting PACD based on 2- and 3-quadrant definitions, the ResNet-18 classifier achieved AUCs of 0.964 and 0.952, respectively, on the test dataset.

Conclusion: Deep learning classifiers effectively detect gonioscopic angle closure and PACD based on automated analysis of AS-OCT images. These methods could be used to automate clinical evaluations of the ACA and improve access to eye care in high-risk populations.

Figure 1:

a) Schematic of binary classification process (top). Unmarked AS-OCT images were used as inputs to the convolutional neural network (CNN) classifiers. Gonioscopy grade probabilities (p₀ to p₄) were summed to make the binary prediction of angle status: angle closure = grades 0 and 1, open angle = grades 2, 3, and 4. b) Representative AS-OCT images corresponding to open (bottom left, grade 4) and closed (bottom right; grade 0) angles based on gonioscopic examination.

Figure 2:

ROC curves of three competing classifiers for detecting gonioscopic angle closure developed using different deep learning architectures: ResNet-18 (blue, AUC = 0.933), custom 14-layer CNN, (green, AUC = 0.910), Inception-v3 (red, AUC = 901). Performance was evaluated on the training dataset.

Figure 3:

ROC curves of the ResNet-18 classifier for detecting gonioscopic angle closure (red, AUC = 0.928) and PACD based on either the two- (blue, AUC = 0.964) or three-quadrant (green, AUC = 0.952) definition. Performance was evaluated on the test dataset.

Figure 4:

Representative saliency maps highlight the pixels that are most discriminative in the prediction of angle closure status by the ResNet-18 classifier. Colormap indicates colors in descending order of salience: white, yellow, red, black.