DRSplus retinographer in patients with diabetes: a comparison study by consensus grading, Reading Center and Artificial Intelligence

Abstract

Purpose: To evaluate the gradable rate of the retinal images acquired with DRSplus retinographer in patients with diabetes and to estimate the diabetic retinopathy (DR) severity, comparing different methods of analysis and grading.

Methods: Prospective, cross-sectional, observational study. A mosaic of overlapped retinal images in non-mydriatic condition was acquired, evaluating the gradable images rate at both image and eye-levels, from the human-study team (consensus grading), Reading Center (RC) and an Artificial Intelligence (AI) system, respectively. The DR severity was graded by the RC and the AI.

Results: 844 images of 422 eyes from 224 patients were included. The gradable rate was 87.4%, 97.4% and 96.9% at image-level and was 81.3%, 97.4% and 95.0% at eye-level, according to the consensus grading, the RC and the AI, respectively. AI sensitivity for detecting referable DR was 97.3%, the specificity was 80.4% and the accuracy was 86.6%.

Conclusions: DRSplus retinographer allowed to image the retina in non-mydriatic condition in more than 80% of eyes, with a higher gradable rate with RC and AI in comparison with consensus grading. The estimation of the DR severity showed a high sensitivity for referral cases for AI system, indicating the potentialities of telemedicine-based tool for the DR screening.

Keywords: Diabetic retinopathy; gradeability analysis; non-mydriatic fundus camera.