Milea D, Najjar RP, Zhubo J, et al. - In the present study, the researchers sought to analyze the use of artificial intelligence to detect papilledema and other optic-disk abnormalities from fundus photographs. The training and validation data sets from 6,779 individuals involved 14,341 photographs: 9,156 of normal disks, 2,148 of disks with papilledema, and 3,037 of disks with other abnormalities. According to findings, a deep-learning system that uses fundus photographs with pharmacologically dilated pupils distinguished between optic disks with papilledema, normal disks, and disks with nonpapilledema abnormalities.