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RESEARCH PRODUCT

Use in clinical practice of an automated screening method of diabetic retinopathy that can be derived using a diagnostic artificial intelligence system

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description

Abstract Background and Objective To compare the diagnostic performance of an autonomous diagnostic artificial intelligence (AI) system for the diagnosis of derivable diabetic retinopathy (RDR) with manual classification. Materials and Methods Patients with type 1 and type 2 diabetes participated in a diabetic retinopathy (RD) screening program between 2011–2012. 2 images of each eye were collected. Unidentifiable retinal images were obtained, one centered on the disc and one on the fovea. The exams were classified with th e autonomous AI system and manually by anonymous ophthalmologists. The results of the AI system and manual classification were compared in terms of sensitivity and specificity for the diagnosis of both (RDR) and diabetic retinopathy with decreased vision (VTDR). Results 10.257 retinografias of 5.360 eyes of 2680 subjects were included. According to the manual classification, the prevalence of RDR was 4.14% and that of VTDR 2.57%. The AI system recorded 100% (95% CI: 97% -100%) sensitivity and 81.82% (95% CI: 80% -83%) specificity for RDR, and 100% (95% CI: 95% -100%) of sensitivity and 94.64% (95% CI: 94% -95%) of specificity for VTDR. Conclusions Compared to the manual classification, the autonomous diagnostic AI system registered a high sensitivity (100%) and specificity (82%) in the diagnosis of RDR and macular edema in people with diabetes. Due to its immediate diagnosis, the autonomous diagnostic AI system can increase the accessibility of RDR screening in primary care settings.