Uso en la práctica clínica, de un método de cribado automatizado de retinopatía diabética derivable mediante un sistema de inteligencia artificial de diagnóstico

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

Cristina Peris-Martínez*, Abhay Shaha, Warren Clarida, Ryan Amelon, María C. Hernáez-Ortega, Amparo Navea, Jesús Morales-Olivas, Rosa Dolz-Marco, Pablo Pérez-Jordá, Frank Verbraak, Amber A. van der Heijden

*Corresponding author for this work

Research output: Contribution to journalArticleProfessional

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 (DR) 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 the 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 retinal inages of 5,630 eyes of 2,680 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.
Translated title of the contributionUse in clinical practice of an automated screening method of diabetic retinopathy that can be derived using a diagnostic artificial intelligence system
Original languageSpanish
JournalArchivos de la Sociedad Espanola de Oftalmologia
Early online date2020
DOIs
Publication statusE-pub ahead of print - 2020

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