Abstract:  Objective To study a deep learning system (DLS) based on convolutional neural network (CNN) for automated detection of glaucomatous optic neuropathy, and to perform a prediction visualization test that can identify regions of the fundus images. Design Cross-sectional study. Participants Ocular fundus photos of 10296 eyes of 5148 patients during 2014 to 2018 in Beijing Tongren Hospital. Methods A deep learning algorithm based on ResNet was trained on the premise that only disease or not can be provided as a marker, then the accuracy, sensitivity and specificity of the algorithm were calculated to evaluate the performance of the trained system for automatic diagnosis. To better understand the process, a prediction visualization test was performed based on a t-distributed stochastic neighbor embedding（t-SNE）visualization that identified regions of the fundus images utilized for diagnosis, and a heatmap was created. Main Outcome Measures Area under the receiver operating characteristics curve (AUC), sensitivity and specificity for DLS with reference to professional graders, diagnosis accuracy and consistency with ophthalmologists according to the heatmap. Results The AUC of the glaucoma diagnosis with CNN (GD-CNN) model in validation datasets was 0.996 (95%CI, 0.995-0.998). The sensitivity and specificity were comparable with that of trained professional graders (sensitivity, 96.2% vs. 96.0%, P=0.76; specificity, 97.7% vs. 97.9%, P=0.81). An accuracy of 100% was presented in areas containing optic nerve head variance and neuroretinal rim loss, and the regions of interest identified to have made the greatest contribution to the neural network’s diagnosis were also shared with 91.8% of ophthalmologists. Conclusion The DLS has high sensitivity and specificity for detecting glaucomatous optic neuropathy. Based on t-SNE, visualization maps are generated from deep features, which can be superimposed on the input image to highlight the areas of the model important for diagnosis. (Ophthalmol CHN, 2020, 29: 9-14)

Key words: artificial intelligence, convolutional neural network, glaucomatous optic neuropathy