  By 2040, age-related macular degeneration (AMD) will affect approximately 288
million people worldwide. Identifying individuals at high risk of progression
to late AMD, the sight-threatening stage, is critical for clinical actions,
including medical interventions and timely monitoring. Although deep learning
has shown promise in diagnosing/screening AMD using color fundus photographs,
it remains difficult to predict individuals' risks of late AMD accurately. For
both tasks, these initial deep learning attempts have remained largely
unvalidated in independent cohorts. Here, we demonstrate how deep learning and
survival analysis can predict the probability of progression to late AMD using
3,298 participants (over 80,000 images) from the Age-Related Eye Disease
Studies AREDS and AREDS2, the largest longitudinal clinical trials in AMD. When
validated against an independent test dataset of 601 participants, our model
achieved high prognostic accuracy (five-year C-statistic 86.4 (95% confidence
interval 86.2-86.6)) that substantially exceeded that of retinal specialists
using two existing clinical standards (81.3 (81.1-81.5) and 82.0 (81.8-82.3),
respectively). Interestingly, our approach offers additional strengths over the
existing clinical standards in AMD prognosis (e.g., risk ascertainment above
50%) and is likely to be highly generalizable, given the breadth of training
data from 82 US retinal specialty clinics. Indeed, during external validation
through training on AREDS and testing on AREDS2 as an independent cohort, our
model retained substantially higher prognostic accuracy than existing clinical
standards. These results highlight the potential of deep learning systems to
enhance clinical decision-making in AMD patients.
