RESUMEN
PURPOSE: To explore the potential of artificial intelligence (AI) for glaucoma detection using deep learning algorithm and evaluate its accuracy for image classification of glaucomatous optic neuropathy (GON) from color fundus photographs. METHODS: A total of 1375 color fundus photographs, 735 normal optic nerve head and 640 GON, were uploaded on the AI software for training, validation, and testing using deep learning model, which is based on Residual Network (Res Net) 50V2. For initial training and validation, 400 fundus images (200 normal and 200 GON) were uploaded and for the final training and testing 975 (535 normal and 440 GON) were uploaded later. Accuracy, sensitivity, and specificity were used to evaluate the image classification performance of the algorithm. Also, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio were calculated. RESULTS: The model used in the study showed an image classification accuracy of 81.3%, sensitivity of 83%, and specificity of 80% for the detection of GON. The false-negative grading was 17% and false-positive grading was 20% for the image classification of GON. Coexistence of glaucoma in patients with high myopia, early glaucoma in a small disc, and software misclassification of GON were the reasons for false-negative results. Physiological large cupping in a large disc, myopic or titled disc, and software misclassification of normal optic disc were the reasons for false-positive results. CONCLUSION: The model employed in this study achieved a good accuracy, and hence has a good potential in detection of GON using color fundus photographs.