The SEE Study: Safety, Efficacy, and Equity of Implementing Autonomous Artificial Intelligence for Diagnosing Diabetic Retinopathy in Youth

Chrystal Thomas,Ingrid Zimmer Galler,Kerry Smith,Laura Prichett,Michael D Abramoff,Risa M Wolf,Roomasa Channa,T Y Alvin Liu

doi:10.2337/figshare.13484976.v1

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 non-mydriatic 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: 310 youth aged 5-21 with diabetes were included, of whom 4.2% had DR. Diagnosability of AI was 97.5% (302/310). The sensitivity and specificity of AI to detect more than mild DR was 85.7% (95% CI: 42.1%- 99.6%) and 79.3 % (95% CI: 74.3%-83.8%) compared to the reference standard as defined by retina specialists. Adherence improved from 49% to 95% after AI implementation. Conclusions: Use of a non-mydriatic 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.

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