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To assess the performance of a deep learning algorithm for detecting referable glaucomatous optic neuropathy (GON) based on color fundus photographs.

A deep learning system for the classification of GON was developed for automated classification of GON on color fundus photographs.

We retrospectively included 48 116 fundus photographs for the development and validation of a deep learning algorithm.

This study recruited 21 trained ophthalmologists to classify the photographs. Referable GON was defined as vertical cup-to-disc ratio of 0.7 or more and other typical changes of GON. The reference standard was made until 3 graders achieved agreement. A separate validation dataset of 8000 fully gradable fundus photographs was used to assess the performance of this algorithm.

The area under receiver operator characteristic curve (AUC) with …