Discrimination Ability of Glaucoma via DCNNs Models from Ultra-Wide Angle Fundus Images Comparing Either Full or Confined to the Optic Disc

Abstract

We examined the difference in ability to discriminate glaucoma among artificial intelligence models trained with partial area surrounding the optic disc (Cropped) and whole area of a ultra-wide angle ocular fundus camera (Full). 1677 normal fundus images and 950 glaucomatous fundus images of the Optos 200Tx (Optos PLC, Dunfermline, United Kingdom) images in the Tsukazaki Hospital ophthalmology database were included in the study. A k-fold method (k = 5) and a convolutional neural network (VGG16) were used. For the full data set, the area under the curve (AUC) was 0.987 (95% CI 0.983–0.991), sensitivity was 0.957 (95% CI 0.942–0.969), and specificity was 0.947 (95% CI 0.935–0.957). For the cropped data set, AUC was 0.937 (95% CI 0.927–0.949), sensitivity was 0.868 (95% CI 0.845–0.889), and specificity was 0.894 (95% CI 0.878–0.908). The values of AUC, sensitivity, and specificity for the cropped data set were lower than those for the full data set. Our results show that the whole ultra-wide angle fundus is more appropriate as the amount of information given to a neural network for the discrimination of glaucoma than only the range limited to the periphery of the optic disc.