Screening for diabetic retinopathy using an automated diagnostic system based on deep learning: Diagnostic accuracy assessment

Sílvia Rêgo, Marco Dutra Medeiros, Filipe Soares, Matilde Monteiro-Soares

Research output: Contribution to journalArticle

Abstract

PURPOSE: To evaluate the diagnostic accuracy of a diagnostic system software for the automated screening of diabetic retinopathy (DR) on digital colour fundus photographs, the 2019 Convolutional Neural Network (CNN) model with Inception-V3.

METHODS: In this cross-sectional study 295 fundus images were analysed by the CNN model and compared to a panel of ophthalmologists. Images were obtained from a dataset acquired within a screening programme. Diagnostic accuracy measures and respective 95% confidence intervals (CI) were calculated.

RESULTS: The sensitivity and specificity of the CNN model in diagnosing referable DR was 81% [95% confidence interval (CI), 66%-90%] and 97% (95% CI, 95%-99%), respectively. Positive predictive value was 86% (95% CI, 72%-94%) and negative predictive value 96% (95% CI, 93%-98%). The positive likelihood ratio was 33 (95% CI, 15-75) and the negative was 0.20 (95% CI, 0.11-0.35). Its clinical impact is demonstrated by the change observed in the pre-test probability of referable DR (assuming a prevalence of 16%) to a post-test probability for a positive test result of 86% and for a negative test result of 4%.

CONCLUSION: A CNN model negative test result safely excludes DR and its use may significantly reduce the burden of ophthalmologists at reading centres.

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