  The purpose of this study is to evaluate the performance of the OphtAI system
for the automatic detection of referable diabetic retinopathy (DR) and the
automatic assessment of DR severity using color fundus photography. OphtAI
relies on ensembles of convolutional neural networks trained to recognize eye
laterality, detect referable DR and assess DR severity. The system can either
process single images or full examination records. To document the automatic
diagnoses, accurate heatmaps are generated. The system was developed and
validated using a dataset of 763,848 images from 164,660 screening procedures
from the OPHDIAT screening program. For comparison purposes, it was also
evaluated in the public Messidor-2 dataset. Referable DR can be detected with
an area under the ROC curve of AUC = 0.989 in the Messidor-2 dataset, using the
University of Iowa's reference standard (95% CI: 0.984-0.994). This is
significantly better than the only AI system authorized by the FDA, evaluated
in the exact same conditions (AUC = 0.980). OphtAI can also detect
vision-threatening DR with an AUC of 0.997 (95% CI: 0.996-0.998) and
proliferative DR with an AUC of 0.997 (95% CI: 0.995-0.999). The system runs in
0.3 seconds using a graphics processing unit and less than 2 seconds without.
OphtAI is safer, faster and more comprehensive than the only AI system
authorized by the FDA so far. Instant DR diagnosis is now possible, which is
expected to streamline DR screening and to give easy access to DR screening to
more diabetic patients.
