Publication

Validation of Automated Screening for Referable Diabetic Retinopathy With an Autonomous Diagnostic Artificial Intelligence System in a Spanish Population

Abhay Shah ¹, Warren Clarida ¹, Ryan Amelon ¹, Maria C Hernaez-Ortega ², Amparo Navea ^3 4 5, Jesus Morales-Olivas ³, Rosa Dolz-Marco ³, Frank Verbraak ⁶, Pablo P Jorda ³, Amber A van der Heijden ^7 8, Cristina Peris Martinez ^3 9

The purpose of this study is to compare the diagnostic performance of an autonomous artificial intelligence (AI) system for the diagnosis of referable diabetic retinopathy (RDR) to manual grading by Spanish ophthalmologists.

Subjects with type 1 and 2 diabetes participated in a diabetic retinopathy (DR) screening program in 2011 to 2012 in Valencia (Spain), and two images per eye were collected according to their standard protocol. Mydriatic drops were used in all patients. Retinal images-one disc and one fovea centered-were obtained under the Medical Research Ethics Committee approval and de-identified. Exams were graded by the autonomous AI system (LumineticsCore™ (formerly known as IDx-DR), Coralville, Iowa, United States), and manually by masked ophthalmologists using adjudication. The outputs of the AI system and manual adjudicated grading were compared using sensitivity and specificity for diagnosis of both RDR and vision-threatening diabetic retinopathy (VTDR).