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

自主人工智能可减少 COVID-19 期间和之后青年糖尿病视网膜病变筛查的差异

• 批准号: 10689400
• 负责人: Risa Michelle Wolf
• 金额: $ 20.13万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10689400
• 关键词: Address; Administrative Supplement; Adult; Affect; Age; Artificial Intelligence; Award; Blindness; COVID-19; COVID-19 impact; Caring; Child; Childhood; Childhood diabetes; Clinic; Clinical; Clinical Data; Clinical Trials; Communities; Complications of Diabetes Mellitus; Computer software; Consensus; Cost Savings; Data; Data Set; Decision Modeling; Detection; Diabetes Mellitus; Diabetic Retinopathy; Early Diagnosis; Effectiveness; Evaluation; Expert Systems; Eye; FDA approved; Foundations; Funding; Fundus photography; Future; Health; Health Services Accessibility; Health Technology; Healthcare Systems; Household; Improve Access; Income; Infrastructure; Institution; Insulin-Dependent Diabetes Mellitus; Insurance; Internet; Intervention; Low income; Medicaid; Methods; Minority; Multicenter Studies; Non-Insulin-Dependent Diabetes Mellitus; Ophthalmic examination and evaluation; Ophthalmologist; Parents; Participant; Patient-Focused Outcomes; Patients; Pediatrics; Persons; Pilot Projects; Population; Prevalence; Randomized Controlled Trials; Research Personnel; Risk; Safety; Screening Result; Site; System; Technology; Telemedicine; Time; United States National Institutes of Health; Youth; adherence rate; arm; behavioral economics; care outcomes; cost; cost effective; cost effectiveness; diabetic; digital healthcare; disparity elimination; experience; follow-up; health care delivery; high risk; improved; innovation; minority children; multidisciplinary; pandemic disease; point of care; prevent; prospective; racial bias; response; retinal imaging; routine care; routine screening; screening; screening guidelines; social; socioeconomics; standard of care; success; underserved community; underserved minority

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.

项目摘要 糖尿病性视网膜病影响4-15％的1型和2型糖尿病的年轻人，是主要原因 成年人的失明早在20岁时。建议对DR进行年度筛查，但只有35-72％ 接受筛查，来自较低社会经济背景的少数族裔青年和儿童不太可能 进行筛选。通过筛查对DR的早期检测可防止视力丧失的发展。电流 小儿DR筛查的护理标准是转介到ECP进行扩张的眼科检查。 2018年，FDA 批准了第一个自主人工智能（AI）软件，该软件解释了用 非肌动式眼底摄像机，为DR筛查的直接结果（POC）提供了直接的结果 成人糖尿病。在我们机构的一项试点研究中，我们是第一个在 儿科，表现出对患者的安全性，有效性和公平性以及节省成本。我们还发现 少数族裔青年，家庭收入较低和医疗补助保险的人不太可能接受 建议筛选，但更有可能拥有DR。 我们假设在糖尿病护理环境中实施POC自治AI将会 提高糖尿病青年的DR筛查率，减轻访问筛查的差异，并成为 对医疗保健系统的成本效益。在父母奖中，AIM1是两个在两个的随机控制试验 与ECP相比，诊所站点以确定自动AI是否增加了筛查，以及那些筛选的人是否增加了 AI的阳性更有可能在ECP进行后续行动。 AIM2是AI的前瞻性观察试验 筛选以确定AI是否减轻筛查的差异，并提高处于危险的少数群体的比例 和低收入的年轻人，如果他们的AI屏幕是积极的，那么他们就要进行后续行动。在AIM 3中，我们将使用决策模型 确定AI是否具有成本效益并节省了医疗保健系统的成本。 如果显示AI在减轻访问护理方面的差异时增加了筛查率，则具有 现在和将来重塑筛查方法的潜力，并将对改善护理产生重大影响 对于服务不足的少数民族和低收入青年。 在此父母奖的此行政补充中，我们要求进行更多支持以进行 父母奖励的目的并传播结果，以及创建高质量的资金 前瞻性收集的小儿视网膜图像数据集，并具有相应的临床数据 由其他调查员。