Detecting Anomalies in Retinal Diseases Using Generative, Discriminative, and Self-supervised Deep Learning

Question  Can artificial intelligence systems trained on normal data recognize anomalies in retinal images?

Findings  In this cross-sectional study of 88 692 high-resolution retinal images of 44 346 individuals with varying severity of diabetic retinopathy, novel anomaly detectors were developed and tested to a surrogate problem using EyePACS data, wherein detectors were trained only on normal retinas (nonreferable diabetic retinopathy) and subsequently tasked to detect abnormality (referable diabetic retinopathy). The detectors had a relatively high area under the receiver operating characteristic curve.

Meaning  This surrogate example suggests anomaly detectors, not trained with diseased retina, can detect diabetic retinopathy; these detectors might play a role by training on normal retina to detect retinal anomalies or novel retinal disease presentations.

Importance  Anomaly detectors could be pursued for retinal diagnoses based on artificial intelligence systems that may not have access to training examples for all retinal diseases in all phenotypic presentations. Possible applications could include screening of population for any retinal disease rather than a specific disease such as diabetic retinopathy, detection of novel retinal diseases or novel presentations of common retinal diseases, and detection of rare diseases with little or no data available for training.

Objective  To study the application of anomaly detection to retinal diseases.

Design, Setting, and Participants  High-resolution retinal images from the publicly available EyePACS data set with fundus images with a corresponding label ranging from 0 to 4 for representing different severities of diabetic retinopathy. Sixteen variants of anomaly detectors were designed. For evaluation, a surrogate problem was constructed, using diabetic retinopathy images, in which only retinas with nonreferable diabetic retinopathy, ie, no diabetic macular edema, and no diabetic retinopathy or mild to moderate nonproliferative diabetic retinopathy were used for training an artificial intelligence system, but both nonreferable and referable diabetic retinopathy (including diabetic macular edema or proliferative diabetic retinopathy) were used to test the system for detecting retinal disease.

Main Outcomes and Measures  Anomaly detectors were evaluated by commonly accepted performance metrics, including area under the receiver operating characteristic curve, F1 score, and accuracy.

Results  A total of 88 692 high-resolution retinal images of 44 346 individuals with varying severity of diabetic retinopathy were analyzed. The best performing across all anomaly detectors had an area under the receiver operating characteristic of 0.808 (95% CI, 0.789-0.827) and was obtained using an embedding method that involved a self-supervised network.

Conclusions and Relevance  This study suggests when abnormal (diseased) data, ie, referable diabetic retinopathy in this study, were not available for training of retinal diagnostic systems wherein only nonreferable diabetic retinopathy was used for training, anomaly detection techniques were useful in identifying images with and without referable diabetic retinopathy. This suggests that anomaly detectors may be used to detect retinal diseases in more generalized settings and potentially could play a role in screening of populations for retinal diseases or identifying novel diseases and phenotyping or detecting unusual presentations of common retinal diseases.