  Purpose: To assess the utility of deep learning in the detection of
geographic atrophy (GA) from color fundus photographs; secondary aim to explore
potential utility in detecting central GA (CGA). Design: A deep learning model
was developed to detect the presence of GA in color fundus photographs, and two
additional models to detect CGA in different scenarios. Participants: 59,812
color fundus photographs from longitudinal follow up of 4,582 participants in
the AREDS dataset. Gold standard labels were from human expert reading center
graders using a standardized protocol. Methods: A deep learning model was
trained to use color fundus photographs to predict GA presence from a
population of eyes with no AMD to advanced AMD. A second model was trained to
predict CGA presence from the same population. A third model was trained to
predict CGA presence from the subset of eyes with GA. For training and testing,
5-fold cross-validation was employed. For comparison with human clinician
performance, model performance was compared with that of 88 retinal
specialists. Results: The deep learning models (GA detection, CGA detection
from all eyes, and centrality detection from GA eyes) had AUC of 0.933-0.976,
0.939-0.976, and 0.827-0.888, respectively. The GA detection model had
accuracy, sensitivity, specificity, and precision of 0.965, 0.692, 0.978, and
0.584, respectively. The CGA detection model had equivalent values of 0.966,
0.763, 0.971, and 0.394. The centrality detection model had equivalent values
of 0.762, 0.782, 0.729, and 0.799. Conclusions: A deep learning model
demonstrated high accuracy for the automated detection of GA. The AUC was
non-inferior to that of human retinal specialists. Deep learning approaches may
also be applied to the identification of CGA. The code and pretrained models
are publicly available at https://github.com/ncbi-nlp/DeepSeeNet.
