Automatic Screening for Ocular Anomalies Using Fundus Photographs. Issue 3 (13th March 2022)

• Record Type: Journal Article
• Authors: Matta, Sarah; Lamard, Mathieu; Conze, Pierre-Henri; Le Guilcher, Alexandre; Ricquebourg, Vincent; Benyoussef, Anas-Alexis; Massin, Pascale; Rottier, Jean-Bernard; Cochener, Béatrice; Quellec, Gwenolé
• Is Part Of: Optometry and vision science. Volume 99:Issue 3(2022)
• Page Start: 281
• Page End: 291
• Publication Date: 2022-03-13
• Subjects: Optometry -- Periodicals; Physiological optics -- Periodicals; Vision disorders -- Periodicals; 617.7505
• DOI: 10.1097/OPX.0000000000001845
• Languages: English
• ISSNs: 1040-5488

Abstract

Abstract : 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: This study aimed to develop a deep learning algorithm that detects any ocular anomaly in fundus photographs and to evaluate this algorithm for "normal versus anomalous" eye examination classification in the diabetic and general populations. METHODS: The deep learning algorithm was developed and evaluated in two populations: the diabetic and general populations. Our patient cohorts consist of 37, 129 diabetic patients from the OPHDIAT diabetic retinopathy screening network in Paris, France, and 7356 general patients from the OphtaMaine private screening network, in Le Mans, France. Each data set was divided into a development subset and a test subset of more than 4000 examinations each. For ophthalmologist/algorithm comparison, a subset of 2014 examinations from the OphtaMaine test subset was labeled 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 the receiver operating characteristic curve for normal versus anomalous classification was 0.9592. On the OphtaMaine test subset, the area under the receiver operating characteristic curve 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 versus anomalous eye examination classification using fundus photography. Artificial intelligence, which previously targeted a few retinal pathologies, can be used to screen for ocular anomalies comprehensively.