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: 10309013
• 负责人: Risa Michelle Wolf
• 金额: $ 49.48万
• 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/10309013
• 关键词: Address; Adult; Affect; Age; Artificial Intelligence; Blindness; COVID-19; COVID-19 pandemic; Caring; Child; Childhood; Childhood diabetes; Clinic; Communities; Complications of Diabetes Mellitus; Computer software; Consensus; Cost Savings; Data; Decision Modeling; Detection; Diabetes Mellitus; Diabetic Retinopathy; Early Diagnosis; Effectiveness; Emergency Situation; Evaluation; Expert Systems; Eye; FDA approved; 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; Ocular Prosthesis; Ophthalmic examination and evaluation; Ophthalmologist; Participant; Patient-Focused Outcomes; Patients; Pediatrics; Persons; Phase; Pilot Projects; Population; Prevalence; Randomized; Risk; Safety; Screening Result; Site; System; Technology; Telemedicine; Time; United States National Institutes of Health; Visit; Youth; adherence rate; arm; behavioral economics; care outcomes; control trial; cost; cost effective; cost effectiveness; diabetic; digital healthcare; disparity elimination; economic impact; experience; follow-up; glycemic control; health care delivery; high risk; improved; innovation; innovative technologies; low socioeconomic status; multidisciplinary; new technology; 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; trial comparing; underserved community

Project Summary COVID-19 has led to disruptions and delays in routine pediatric care. For children with diabetes who see their diabetes team every 3 months, this has been reduced or transitioned to telemedicine due to COVID- 19. However, those without technology and internet capabilities, namely low income and minority youth, are less likely to participate in telemedicine and may see their diabetes team less frequently during the pandemic. Screening for diabetes complications, such as diabetic retinopathy (DR), is generally fulfilled by a separate visit to an eye-care professional (ECP), and is also less likely to occur during COVID-19. 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. Recently, 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. This is likely to worsen due to the disparate effects of COVID-19. 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 now and beyond the COVID-19 pandemic. In this proposal, we will first determine (Aim1) in a randomized control trial at two clinic sites 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. In the second phase of this proposal (Aim2) we will perform 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.

项目摘要 新冠肺炎导致常规儿科护理中断和延误。适用于患有糖尿病的儿童 每3个月见一次他们的糖尿病团队，由于COVID，这已经减少或过渡到远程医疗- 19.然而，那些没有技术和互联网能力的人，即低收入和少数族裔青年，是 不太可能参加远程医疗，在大流行期间可能不太频繁地看到他们的糖尿病团队。 糖尿病并发症的筛查，如糖尿病视网膜病变(DR)，通常通过单独的检查来完成 到眼部护理专业人员(ECP)，也较少发生在新冠肺炎期间。 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。最近，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，但更有可能患有Dr.由于不同的影响，这种情况可能会恶化 新冠肺炎。 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 现在和超越新冠肺炎大流行，对卫生保健系统具有成本效益。在这项提案中，我们 将首先在两个临床地点的随机对照试验中确定(Aim1)自主人工智能是否增加筛查 与ECP相比，如果那些通过人工智能筛查呈阳性的人更有可能在ECP进行跟进。在 本计划的第二阶段(AIM2)我们将进行人工智能筛查的前瞻性观察性试验，以 确定人工智能是否缩小了筛查中的差异，并提高了高危、少数和低风险人群的比例 收入，年轻人谁去跟进，如果他们的人工智能屏幕是积极的。在目标3中，我们将使用决策模型来 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。如果显示人工智能增加 筛查率虽然减少了获得医疗保健的差距，但它有可能重塑筛查方法 无论是现在还是将来。