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

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% CI were calculated. Results: 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%. Conclusion: A CNN model negative test result safely excludes DR, and its use may significantly reduce the burden of ophthalmologists at reading centres.

Automated summary

The study evaluated the diagnostic accuracy of a CNN model for automated screening of diabetic retinopathy. The CNN model showed high sensitivity and specificity in diagnosing referable DR, which can significantly reduce the burden on ophthalmologists.