Detecting Glaucoma in Highly Myopic Eyes From Fundus Photographs Using Deep Convolutional Neural Networks

High myopia (HM) is a major risk factor for glaucoma. However, glaucomatous optic neuropathy is often undiagnosed owing to atypical structural alterations with axial elongation. Moreover, an algorithm to detect glaucoma in highly myopic eyes has not yet been reported.

We recruited 2643 colour fundus photographs to train a ResNet-50 network for discriminating eyes with highly myopic glaucoma (HMG) from HM or glaucoma alone. We employed a 10-fold cross-validation strategy to evaluate the model's performance and applicability across diverse patient groups. Multiple metrics were computed to gauge the model's diagnostic process. The diagnostic ability of the model was then juxtaposed with those made by ophthalmologists to determine concordance. The gradient-weighted class activation maps were used for visual explanations.

Our model demonstrated an overall accuracy of 97.7% with an area under the curve of 98.6% (sensitivity, 91.2%; specificity, 98.0%) for the differential diagnosis among HM, glaucoma, HMG and normal controls. These metrics notably outperformed the diagnostic performances of two attending ophthalmologists, who achieved accuracies of 64.7% and 69.9%. The activation maps derived from the model suggested that the most discriminative lesions for diagnosing HMG were predominantly in the disc, peripapillary area and inferior region of the disc, which are often displayed with a tessellated fundus. These results were slightly different from the understanding of the attending ophthalmologists.

Our proposed model demonstrates high efficacy and suggests specific features for distinguishing eyes with HMG, enabling potential clinical value in assisting the intricate diagnosis of this vision-threatening disease.