  In assessing the severity of age-related macular degeneration (AMD), the
Age-Related Eye Disease Study (AREDS) Simplified Severity Scale predicts the
risk of progression to late AMD. However, its manual use requires the
time-consuming participation of expert practitioners. Although several
automated deep learning systems have been developed for classifying color
fundus photographs (CFP) of individual eyes by AREDS severity score, none to
date has used a patient-based scoring system that uses images from both eyes to
assign a severity score. DeepSeeNet, a deep learning model, was developed to
classify patients automatically by the AREDS Simplified Severity Scale (score
0-5) using bilateral CFP. DeepSeeNet was trained on 58,402 and tested on 900
images from the longitudinal follow-up of 4549 participants from AREDS. Gold
standard labels were obtained using reading center grades. DeepSeeNet simulates
the human grading process by first detecting individual AMD risk factors
(drusen size, pigmentary abnormalities) for each eye and then calculating a
patient-based AMD severity score using the AREDS Simplified Severity Scale.
DeepSeeNet performed better on patient-based classification (accuracy = 0.671;
kappa = 0.558) than retinal specialists (accuracy = 0.599; kappa = 0.467) with
high AUC in the detection of large drusen (0.94), pigmentary abnormalities
(0.93), and late AMD (0.97). DeepSeeNet demonstrated high accuracy with
increased transparency in the automated assignment of individual patients to
AMD risk categories based on the AREDS Simplified Severity Scale. These results
highlight the potential of deep learning to assist and enhance clinical
decision-making in patients with AMD, such as early AMD detection and risk
prediction for developing late AMD. DeepSeeNet is publicly available on
https://github.com/ncbi-nlp/DeepSeeNet.
