Autonomous AI to mitigate disparities for diabetic retinopathy screening in youth during and after COVID-19

项目总结

Project Summary Diabetic retinopathy affects 4-15% of youth with type 1 and type 2 diabetes and is a leading cause of blindness in adults as early as age 20. Yearly screening for DR is recommended, but only 35-72% of youth undergo screening, with minority youth and children from lower socioeconomic backgrounds less likely to undergo screening. Early detection of DR through screening prevents progression to vision loss. The current standard of care for pediatric DR screening is referral to an ECP for a dilated eye exam. In 2018, the FDA approved the first autonomous artificial intelligence (AI) software that interprets retinal images taken with a non-mydriatic fundus camera, providing an immediate result for DR screening at the point of care (POC) for adults with diabetes. In a pilot study at our institution, we were the first to implement this technology in pediatrics, demonstrating safety, effectiveness and equity, and cost-savings to the patient. We also found that minority youth, those with lower household income and Medicaid insurance were less likely to undergo recommended screening, yet were more likely to have DR. We hypothesize that implementing POC autonomous AI in the diabetes care setting will increase DR screening rates in youth with diabetes, mitigate disparities in access to screening, and be cost-effective to the health care system. In the parent award, Aim1 is a randomized control trial at two clinic sites to determine if autonomous AI increases screening compared to ECP, and if those who screen positive by AI are more likely to go for follow-up at the ECP. Aim2 is a prospective observational trial of AI screening to determine if AI mitigates disparities in screening, and improves the proportion of at-risk, minority and low income, youth who go for follow-up if their AI screen is positive. In Aim 3, we will use a decision model to determine if AI is cost-effective and cost-savings to the health care system. If AI is shown to increase screening rates while mitigating disparities in access to care, it has the potential to reshape screening methods now and in the future, and will have a major impact on improving care for underserved minority and low-income youth. In this administrative supplement to the parent award, we are requesting additional support to conduct the aims of the parent award and disseminate the results, as well as funds to create a high-quality prospectively collected dataset of pediatric retinal images with corresponding clinical data that can be utilized by other investigators.