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

项目摘要 糖尿病视网膜病变影响4%-15%患有1型和2型糖尿病的年轻人，是导致 成年人早在20岁就失明。建议每年进行DR筛查，但只有35%-72%的年轻人 接受筛查，少数族裔青年和社会经济背景较低的儿童不太可能 接受筛查。通过筛查及早发现DR可防止进展为视力丧失。海流 儿科DR筛查的标准是转诊到ECP进行扩眼检查。2018年，FDA 批准了第一个自主人工智能(AI)软件，可以解释用 非散瞳眼底相机，为在护理点(POC)进行DR筛查提供即时结果 患有糖尿病的成年人。在我们机构的一项试点研究中，我们是第一个在 儿科，向患者展示安全性、有效性和公平性，并节省成本。我们还发现， 少数族裔青年、家庭收入较低的人和医疗补助保险的人不太可能接受 推荐的筛查，但更有可能是Dr。 我们假设，在糖尿病护理环境中实施POC自主人工智能将 提高青年糖尿病患者的DR筛查率，缓解筛查机会的差距，并 对医疗保健系统具有成本效益。在父母奖中，Aim1是一项随机对照试验，试验时间为2 临床网站，以确定自主人工智能是否比ECP增加筛查，以及那些筛查的人 人工智能呈阳性的患者更有可能在ECP进行后续治疗。AIM2是人工智能的前瞻性观察性试验 筛查以确定人工智能是否减少了筛查中的差异，并提高了高危少数民族的比例 低收入的年轻人，如果他们的人工智能屏幕是阳性的，他们就会去跟进。在目标3中，我们将使用决策模型 以确定人工智能对医疗保健系统是否具有成本效益和成本节约。 如果人工智能被证明提高了筛查率，同时减轻了获得医疗服务的差距，它就有了 有可能重塑现在和未来的筛查方法，并将对改善医疗保健产生重大影响 针对服务不足的少数族裔和低收入青年。 在家长奖励的这一行政补充中，我们请求额外的支持，以进行 目的是为家长颁奖和传播成果，以及为打造优质的资金 前瞻性收集的儿童视网膜图像数据集以及可利用的相应临床数据 由其他调查人员所为。

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.