Abhay Shah1, Warren Clarida1, Ryan Amelon1, Maria C Hernaez-Ortega2, Amparo Navea3,4,5, Jesus Morales-Olivas3, Rosa Dolz-Marco3, Frank Verbraak6, Pablo P Jorda3, Amber A van der Heijden7,8, Cristina Peris Martinez3,9. 1. Dx Technologies Inc, Coralville, IA, USA. 2. European Innovative Biomedicine Institute (EIBI), Cantabria, Spain. 3. FISABIO OFTALMOLOGIA MEDICA (FOM), Valencia, Spain. 4. Instituto de la retina, Valencia, Spain. 5. Universidad Cardenal Herrera CEU, Valencia, Spain. 6. Department of Ophthalmology, VUmc, Amsterdam University Medical Centers, The Netherlands. 7. Department of General Practice and Elderly Care Medicine, VU University Medical Centre, Amsterdam, The Netherlands. 8. Amsterdam Public Health Research Institute, VU University Medical Centre, The Netherlands. 9. University of Valencia, Spain.
Abstract
PURPOSE: 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. METHODS: 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 (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). RESULTS: A total of 2680 subjects were included in the study. According to manual grading, prevalence of RDR was 111/2680 (4.14%) and of VTDR was 69/2680 (2.57%). Against manual grading, the AI system had a 100% (95% confidence interval [CI]: 97%-100%) sensitivity and 81.82% (95% CI: 80%-83%) specificity for RDR, and a 100% (95% CI: 95%-100%) sensitivity and 94.64% (95% CI: 94%-95%) specificity for VTDR. CONCLUSION: Compared to manual grading by ophthalmologists, the autonomous diagnostic AI system had high sensitivity (100%) and specificity (82%) for diagnosing RDR and macular edema in people with diabetes in a screening program. Because of its immediate, point of care diagnosis, autonomous diagnostic AI has the potential to increase the accessibility of RDR screening in primary care settings.
PURPOSE: 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. METHODS: 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 (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). RESULTS: A total of 2680 subjects were included in the study. According to manual grading, prevalence of RDR was 111/2680 (4.14%) and of VTDR was 69/2680 (2.57%). Against manual grading, the AI system had a 100% (95% confidence interval [CI]: 97%-100%) sensitivity and 81.82% (95% CI: 80%-83%) specificity for RDR, and a 100% (95% CI: 95%-100%) sensitivity and 94.64% (95% CI: 94%-95%) specificity for VTDR. CONCLUSION: Compared to manual grading by ophthalmologists, the autonomous diagnostic AI system had high sensitivity (100%) and specificity (82%) for diagnosing RDR and macular edema in people with diabetes in a screening program. Because of its immediate, point of care diagnosis, autonomous diagnostic AI has the potential to increase the accessibility of RDR screening in primary care settings.
Entities:
Keywords:
artificial intelligence; diabetic retinopathy; diabetic retinopathy screening; population screening
Authors: Fernando Korn Malerbi; Rafael Ernane Andrade; Paulo Henrique Morales; José Augusto Stuchi; Diego Lencione; Jean Vitor de Paulo; Mayana Pereira Carvalho; Fabrícia Silva Nunes; Roseanne Montargil Rocha; Daniel A Ferraz; Rubens Belfort Journal: J Diabetes Sci Technol Date: 2021-01-12
Authors: Soledad Jimenez-Carmona; Pedro Alemany-Marquez; Pablo Alvarez-Ramos; Eduardo Mayoral; Manuel Aguilar-Diosdado Journal: J Clin Med Date: 2021-12-21 Impact factor: 4.241