Deep Neural Network for Scleral Spur Detection in Anterior Segment OCT Images: The Chinese American Eye Study.

Purpose: To develop a deep neural network that detects the scleral spur in anterior segment optical coherence tomography (AS-OCT) images.

Methods: Participants in the Chinese American Eye Study, a population-based study in Los Angeles, California, underwent complete ocular examinations, including AS-OCT imaging with the Tomey CASIA SS-1000. One human expert grader provided reference labels of scleral spur locations in all images. A convolutional neural network (CNN)-based on the ResNet-18 architecture was developed to detect the scleral spur in each image. Performance of the CNN model was assessed by calculating prediction errors, defined as the difference between the Cartesian coordinates of reference and CNN-predicted scleral spur locations. Prediction errors were compared with intragrader variability in detecting scleral spur locations by the reference grader.

Results: The CNN was developed using a training dataset of 17,704 images and tested using an independent dataset of 921 images. The mean absolute prediction errors of the CNN model were 49.27 ± 42.07 µm for X-coordinates and 47.73 ± 39.70 µm for Y-coordinates. The mean absolute intragrader variability was 52.31 ± 47.75 µm for X-coordinates and 45.88 ± 45.06 µm for Y-coordinates. Distributions of prediction errors for the CNN and intragrader variability for the reference grader were similar for X-coordinates ( = 0.609) and Y-coordinates ( = 0.378). The mean absolute prediction error of the CNN was 73.08 ± 52.06 µm and the mean absolute intragrader variability was 73.92 ± 60.72 µm.

Conclusions: A deep neural network can detect the scleral spur on AS-OCT images with performance similar to that of a human expert grader.

Translational Relevance: Deep learning methods that automate scleral spur detection can facilitate qualitative and quantitative assessments of AS-OCT images.