Burlina PM, et al. - Researchers studied data collected from November 13, 1992, to November 30, 2005, from 4613 candidates of the Age-Related Eye Disease Study (AREDS) data set to develop deep convolutional neural networks that were trained to provide detailed automated age-related macular degeneration (AMD) grading on several AMD severity classification scales to determine deep learning (DL) techniques for the AREDS 9-step detailed severity scale for AMD to estimate 5-year risk possibility with reasonable accuracy. They observed promising results in providing AMD detailed severity grading (9-step classification). They potentially suggested the use of DL to assist physicians in longitudinal care for individualized, comprehensive risk estimation and clinical studies of disease progression during treatment or a public screening or monitoring worldwide as well.