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Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

JAMA · 2016 · Vol. 316(22) · pp. 2402–2402

Varun Gulshan(Google (United States))Lily Peng✉(Google (United States))Marc Coram(Google (United States))Martin C. Stumpe(Google (United States))Derek Wu(Google (United States))Arunachalam Narayanaswamy(Google (United States))Subhashini Venugopalan(Google (United States))Kasumi Widner(Google (United States))T. Madams(Google (United States))Jorge Cuadros(University of California, Berkeley)Kim Ramasamy(Aravind Eye Hospital)Rajiv Raman(Sankara Nethralaya)Philip Nelson(Google (United States))Jessica L. Mega(Harvard University)Dale R. Webster(Google (United States))

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Abstract

In this evaluation of retinal fundus photographs from adults with diabetes, an algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy. Further research is necessary to determine the feasibility of applying this algorithm in the clinical setting and to determine whether use of the algorithm could lead to improved care and outcomes compared with current ophthalmologic assessment.

Retinal Imaging and Analysis Retinal Diseases and Treatments Retinal and Optic ConditionsMedicineDiabetic retinopathyFundus (uterus)Deep learningConvolutional neural networkArtificial intelligenceAlgorithmRetinalOphthalmologyMacular edema

MeSH terms

Machine LearningOphthalmologistsAlgorithmsDiabetic RetinopathyFemaleFundus OculiHumansMacular EdemaMaleMiddle AgedPhotographySensitivity and SpecificityObserver VariationNeural Networks, Computer

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