Instant automatic diagnosis of diabetic retinopathy

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.