Validation of a Deep Learning Model to Screen for Glaucoma Using Images from Different Fundus Cameras and Data Augmentation

Purpose: To validate a deep residual learning algorithm to diagnose glaucoma from fundus photography using different fundus cameras at different institutes. Design: Cross-sectional study. Participants: A training dataset consisted of 1364 color fundus photographs with glaucomatous indications and 1768 color fundus photographs without glaucomatous features. Two testing datasets consisted of (1) 95 images of 95 glaucomatous eyes and 110 images of 110 normative eyes, and (2) 93 images of 93 glaucomatous eyes and 78 images of 78 normative eyes. Methods: A deep learning algorithm known as Residual Network (ResNet) was used to diagnose glaucoma using a training dataset. The 2 testing datasets were obtained using different fundus cameras (different manufacturers) across multiple institutes. The size of the training data was artificially increased by adding minor alterations to the original data, known as "image augmentation." Diagnostic accuracy was assessed using the area under the receiver operating characteristic curve (AROC). Main Outcome Measures: Area under the receiver operating characteristic curve. Results: When image augmentation was not used, the AROC was 94.8% (90.3-96.8) in the first testing dataset and 99.7% (99.4-100.0) in the second dataset. These AROC values were significantly (P < 0.05) smaller without augmentation (87.7% [82.8-92.6] in the first testing dataset and 94.5% [91.3-97.6] in the second testing dataset). Conclusions: The previously developed deep residual learning algorithm achieved high diagnostic performance with different fundus cameras across multiple institutes, in particular when image augmentation was used. (C) 2019 by the American Academy of Ophthalmology