Screening for Diabetic Retinopathy Using an Automated Diagnostic System Based on Deep Learning: Diagnostic Accuracy Assessment

Abstract

<b><i>Purpose:</i></b> 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. <b><i>Methods:</i></b> 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% CI were calculated. <b><i>Results:</i></b> The sensitivity and specificity of the CNN model in diagnosing referable DR was 81% (95% 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%. <b><i>Conclusion:</i></b> A CNN model negative test result safely excludes DR, and its use may significantly reduce the burden of ophthalmologists at reading centres.