Risa M Wolf1, T Y Alvin Liu2, Chrystal Thomas1, Laura Prichett3, Ingrid Zimmer-Galler2, Kerry Smith2, Michael D Abramoff4,5,6,7,8, Roomasa Channa9,10. 1. Division of Endocrinology, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, MD. 2. Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, MD. 3. Biostatistics, Epidemiology, and Data Management Core, Johns Hopkins School of Medicine, Baltimore, MD. 4. Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA. 5. IDx, Coralville, IA. 6. Iowa City VA Medical Center, Iowa City, IA. 7. Department of Biomedical Engineering, The University of Iowa, Iowa City, IA. 8. Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA. 9. Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, MD rchanna@wisc.edu. 10. Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI.
Abstract
OBJECTIVE: Diabetic retinopathy (DR) is a leading cause of vision loss worldwide. Screening for DR is recommended in children and adolescents, but adherence is poor. Recently, autonomous artificial intelligence (AI) systems have been developed for early detection of DR and have been included in the American Diabetes Association's guidelines for screening in adults. We sought to determine the diagnostic efficacy of autonomous AI for the diabetic eye exam in youth with diabetes. RESEARCH DESIGN AND METHODS: In this prospective study, point-of-care diabetic eye exam was implemented using a nonmydriatic fundus camera with an autonomous AI system for detection of DR in a multidisciplinary pediatric diabetes center. Sensitivity, specificity, and diagnosability of AI was compared with consensus grading by retinal specialists, who were masked to AI output. Adherence to screening guidelines was measured before and after AI implementation. RESULTS: Three hundred ten youth with diabetes aged 5-21 years were included, of whom 4.2% had DR. Diagnosability of AI was 97.5% (302 of 310). The sensitivity and specificity of AI to detect more-than-mild DR was 85.7% (95% CI 42.1-99.6%) and 79.3% (74.3-83.8%), respectively, compared with the reference standard as defined by retina specialists. Adherence improved from 49% to 95% after AI implementation. CONCLUSIONS: Use of a nonmydriatic fundus camera with autonomous AI was safe and effective for the diabetic eye exam in youth in our study. Adherence to screening guidelines improved with AI implementation. As the prevalence of diabetes increases in youth and adherence to screening guidelines remains suboptimal, effective strategies for diabetic eye exams in this population are needed.
OBJECTIVE:Diabetic retinopathy (DR) is a leading cause of vision loss worldwide. Screening for DR is recommended in children and adolescents, but adherence is poor. Recently, autonomous artificial intelligence (AI) systems have been developed for early detection of DR and have been included in the American Diabetes Association's guidelines for screening in adults. We sought to determine the diagnostic efficacy of autonomous AI for the diabetic eye exam in youth with diabetes. RESEARCH DESIGN AND METHODS: In this prospective study, point-of-care diabetic eye exam was implemented using a nonmydriatic fundus camera with an autonomous AI system for detection of DR in a multidisciplinary pediatric diabetes center. Sensitivity, specificity, and diagnosability of AI was compared with consensus grading by retinal specialists, who were masked to AI output. Adherence to screening guidelines was measured before and after AI implementation. RESULTS: Three hundred ten youth with diabetes aged 5-21 years were included, of whom 4.2% had DR. Diagnosability of AI was 97.5% (302 of 310). The sensitivity and specificity of AI to detect more-than-mild DR was 85.7% (95% CI 42.1-99.6%) and 79.3% (74.3-83.8%), respectively, compared with the reference standard as defined by retina specialists. Adherence improved from 49% to 95% after AI implementation. CONCLUSIONS: Use of a nonmydriatic fundus camera with autonomous AI was safe and effective for the diabetic eye exam in youth in our study. Adherence to screening guidelines improved with AI implementation. As the prevalence of diabetes increases in youth and adherence to screening guidelines remains suboptimal, effective strategies for diabetic eye exams in this population are needed.
Authors: Michael D Abràmoff; Cybil Roehrenbeck; Sylvia Trujillo; Juli Goldstein; Anitra S Graves; Michael X Repka; Ezequiel Zeke Silva Iii Journal: NPJ Digit Med Date: 2022-06-09