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Diagnostic Accuracy of Artificial Intelligence-based Automated Detection of Diabetic Retinopathy in Routine Care
Krzysztof Irlik1, Hanna Kwiendacz2, Adam Wylęgała3
1Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland; Doctoral School, Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland.
Purpose:
To evaluate the diagnostic performance of a regulatory-approved (CE-marked) artificial intelligence system (RetCAD) applied to nonmydriatic color fundus photographs for diabetic retinopathy (DR) screening in routine clinical care.
Methods:
This was a prospective single-center observational diagnostic accuracy study including adults with diabetes who underwent nonmydriatic fundus imaging between January 9, 2023 and August 6, 2024. Nonmydriatic true-color confocal fundus photographs were obtained using the iCare DRSplus camera. RetCAD generated a 5-category DR grade and a continuous severity score according to the International Clinical Diabetic Retinopathy scale. Two board-certified ophthalmologists independently graded images; one served as the reference standard, the second reader's grades were used to assess inter-reader agreement using Cohen's kappa. Diagnostic accuracy was evaluated at 3 prespecified thresholds: any DR; moderate DR or worse (referable DR, defined as International Clinical Diabetic Retinopathy grade 2 or above); and severe DR or worse. Receiver operating characteristic curves and area under the curve were derived from the artificial intelligence severity score. Subgroup analyses included age, diabetes duration, estimated glomerular filtration rate, and glycated hemoglobin.
Findings:
609 participants (1218 eyes) were screened; 533 participants (1040 eyes) were included (median age 56 years [interquartile range 42-67], 51% female). For detection of referable DR at the eye level, sensitivity was 0.85 (95% confidence interval [CI] 0.79-0.91) and specificity was 0.97 (95% CI 0.96-0.98). The referral rate was 17.0%, with a reference prevalence of 17.2%. The areas under the curves were 0.85 for any DR, 0.96 for referable DR (moderate DR or worse), and 0.98 for severe DR or worse. At the patient level, sensitivity and specificity for referable DR were 0.89 and 0.95, with a referral rate of 22.0%. Sensitivity was significantly lower in participants aged ≥65 years and in those with estimated glomerular filtration rate <60 mL/min/1.73 m². Inter-reader agreement was high (κ = 0.877 unweighted; κ = 0.954 squared-weighted).
Implications:
RetCAD demonstrated high accuracy and strong discriminative ability for identifying referable DR using nonmydriatic fundus imaging, supporting its use as a triage tool for ophthalmic referral in routine clinical practice.
