Automatic screening for ocular anomalies using fundus photographs
Résumé
Significance
Screening for ocular anomalies using fundus photography is key to prevent vision impairment and blindness. With the growing and aging population, automated algorithms that can triage fundus photographs and provide instant referral decisions are relevant to scale-up screening and face the shortage of ophthalmic expertise.
Purpose
To develop a deep learning algorithm that detects any ocular anomaly in fundus photographs. To evaluate this algorithm for ‘normal vs. anomalous’ eye exam classification in the diabetic and general populations.
Methods
The deep learning algorithm was developed and evaluated in two populations: the diabetic and the general population. Our patient cohorts consist of 37,129 diabetic patients from the OPHDIAT diabetic retinopathy screening network in Paris, France, and 7,356 general patients from the OphtaMaine private screening network, in Le Mans, France. Each dataset was divided into a development subset and a test subset of more than 4,000 examinations each. For ophthalmologist/algorithm comparison, a subset of 2,014 examinations from the OphtaMaine test subset was labelled by a second ophthalmologist. First, the algorithm was trained on the OPHDIAT development subset. Then, it was fine-tuned on the OphtaMaine development subset.
Results
On the OPHDIAT test subset, the area under receiver operating characteristic curve (AUC) for ‘normal vs. anomalous’ classification was 0.9592. On the OphtaMaine test subset, the AUC was 0.8347 before fine-tuning and 0.9108 after fine-tuning. On the ophthalmologist/algorithm comparison subset, the second ophthalmologist achieved a specificity of 0.8648 and a sensitivity of 0.6682. For the same specificity, the fine-tuned algorithm achieved a sensitivity of 0.8248.
Conclusions
The proposed algorithm compares favorably with human performance for ‘normal vs. anomalous’ eye exam classification using fundus photography. Artificial intelligence, which previously targeted a few retinal pathologies, can be used to screen for ocular anomalies comprehensively.