Abstract
Purpose :
The aim of this study was to apply an artificial intelligence (AI) algorithm, through deep learning, for the optimized development of an automated diabetic retinopathy (DR) detection algorithm using retinographies and to study the consistency of retina ophthalmologists with the artificial intelligence system in DR screening under routine clinical practice conditions.
Methods :
A clinical practice retinographies dataset were used to train an algorithm formed by two component networks which were independently optimized, with the outputs combined to give a single classification for DR. For evaluation, an international standardized retinography dataset in diabetic retinopathy (Messidor-2) was used, which were evaluated by the AI algorithm and two retinal experts with more than 10 years of experience, from different autonomous regions and health systems and diabetic retinopathy screening programs in a blind and independent manner. No prior unification of diagnostic criteria (DR) was performed among the observers to simulate conditions of routine clinical practice, the grades to be used being: absent DR, mild DR, moderate DR, severe DR and proliferative DR. The comparative analysis was performed by grouping DR grades into two groups: Non-derivable (absent DR and mild DR) and Derivable (mild, moderate, severe and proliferative DR).
Results :
An image training datsetset of 109,628 images was used for the training phase. Both the AI algorithm and retinal experts analyzed the 1744 images in the evaluation dataset each. The consistency results pitting observer 1 and 2 independently against the AI algorithm were as follows, respectively: a sensitivity of 0.99 and 1; a specificity of 0.74 and 0.71; and an area under the ROC curve of 0.87 and 0.86.
Conclusions :
In the current state, our deep learning-based algorithm for retinography-based screening of diabetic retinopathy develops behavior aligned with that of expert retinal ophthalmologists under routine clinical practice conditions. There is the possibility of applying this algorithm in clinical practice with the aim of improving health outcomes compared to the current standard of ophthalmologic management of diabetic patients.
This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.