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: 10598686
• 负责人: Risa Michelle Wolf
• 金额: $ 34.35万
• 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/10598686
• 关键词: Address; Adult; Area; Artificial Intelligence; Award; Biological; Black race; COVID-19; Caring; Clinic; Clinical; Clinical Research; Clinical Trials; Cognitive; Communities; Computer software; Conduct Clinical Trials; Conflict (Psychology); Consensus; Cost Savings; Decision Making; Development; Diabetes Mellitus; Diabetic Retinopathy; Diagnosis; Diagnostic; Effectiveness; Ensure; Equipoise; Ethical Analysis; Ethical Issues; Ethics; Ethnic Origin; Expert Systems; Exposure to; Eye; FDA approved; Fundus photography; Future; Gold; Health Services Accessibility; Healthcare; Healthcare Systems; Household; Image; Income; Individual; Informed Consent; Insurance; Intervention; Interview; Latinx; Low income; Measures; Medicaid; Medicine; Methodology; Methods; Minority; Modeling; Operative Surgical Procedures; Ophthalmology; Outcome; Parents; Participant; Patients; Pediatrics; Performance; Population; Process; Race; Randomized Controlled Trials; Recommendation; Research; Research Personnel; Resolution; Risk; Safety; Sensitivity and Specificity; Series; Site; Subgroup; Technology; Therapeutic Intervention; Thoracic Radiography; Time; Underserved Population; United States National Institutes of Health; Work; Youth; base; computer science; cost effective; design; drug efficacy; efficacy testing; experience; follow-up; improved; minority children; point of care; population health; predictive modeling; pressure; prospective; retinal imaging; risk benefit ratio; screening; screening guidelines; sex; therapy outcome

Project Summary In 2018 the FDA approved the first autonomous healthcare artificial intelligence (AI-HC) software that interprets retinal images taken with a non-mydriatic fundus camera, providing an immediate result for diabetic retinopathy (DR) screening at the point of care (POC) for adults with diabetes. The PIs of the parent award were the first to implement this technology in pediatrics, demonstrating safety, effectiveness and equity, and cost-savings to the patient. They 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. The parent award hypothesizes 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, Aim 1 is a randomized control trial at two clinic sites to determine if autonomous AI increases screening compared to an eye-care professional (ECP), and if those who screen positive by AI are more likely to go for follow-up at the ECP. Aim 2 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. If AI is shown to increase screening rates while mitigating disparities in access to care, it has the potential to reshape screening methods. Partnering with the parent award presents a unique opportunity to address two pressing ethical questions: What are the ethical challenges with conducting clinical trials for AI? How do you anticipate, identify, and address ethical problems with AI-HC before they cause harm? The supplement team has worked closely with the parent award investigators during their F.D.A. review, in the Collaborative Community on Ophthalmic Imaging for the F.D.A. and, from these experiences, in development of a methodology to identify and address ethical challenges with AI for healthcare. In this supplement we will pilot our ethical analysis methodology to: 1) identify the ethical issues emerging with clinical trials of AI-HC for DR in real time as such trials are being conducted through the parent award; 2) develop expert consensus on how to address these identified ethical challenges for the parent award; and 3) in doing 1 & 2, refine a generalizable approach for identifying and addressing ethical challenges with an AI-HC and a roadmap for how to address ethical concerns with future clinical trials of AI-HC.

项目摘要 2018年，FDA批准了解释的第一个自治医疗人工智能（AI-HC）软件 用非肌底面摄像机拍摄的视网膜图像，为糖尿病性视网膜病带来直接的结果 （DR）在糖尿病成年人的护理点（POC）筛查。父母奖的PI是第一个 在儿科中实施这项技术，证明安全性，有效性和公平性以及节省成本 病人。他们还发现，少数族裔青年，家庭收入较低和医疗补助保险的年轻人 不太可能进行建议的筛查，但更有可能拥有DR。父母奖 假设在糖尿病护理环境中实施POC自治AI将增加DR筛查 糖尿病青年的率，减轻访问筛查的差异，对医疗保健具有成本效益 系统。在父母奖中，AIM 1是在两个诊所地点进行的随机控制试验，以确定是否自动 与眼科专业人员（ECP）相比，AI增加了筛查，如果AI筛选阳性的人 更有可能在ECP进行后续行动。 AIM 2是对AI筛查的前瞻性观察试验 确定AI是否减轻了筛查的差异，并提高了处于危险，少数群体和低差异的比例 收入，如果他们的AI屏幕是积极的，那么他们会进行跟进的年轻人。如果显示AI提高筛选率，而 减轻访问护理方面的差异，它具有重塑筛查方法的潜力。 与父母奖合作提供了一个独特的机会，可以解决两个紧迫的道德问题： 对AI进行临床试验的道德挑战是什么？您如何预期，识别和 在造成伤害之前，请先解决AI-HC的道德问题？补充团队与 家长奖调查员在F.D.A.期间在眼科协作社区中进行审查 成像F.D.A.从这些经验中，开发一种方法来识别和解决 AI为医疗保健带来的道德挑战。在这种补充中，我们将试用道德分析方法：1） 通过实时的AI-HC进行AI-HC的临床试验确定伦理问题，因为正在进行此类试验 通过父母奖； 2）建立有关如何解决这些识别道德的专家共识 父母奖的挑战； 3）在做1和2时，请完善一种可识别和 通过AI-HC解决道德挑战和如何解决未来的道德问题的路线图 AI-HC的临床试验。