AI-driven low-cost ultrasound for automated quantification of hypertension, preeclampsia, and IUGR

AI 驱动的低成本超声可自动量化高血压、先兆子痫和 IUGR

• 批准号: 10567313
• 负责人: Gari David Clifford
• 金额: $ 65.53万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10567313
• 关键词: Address; Algorithms; Artificial Intelligence; Birth; Blood Pressure; Caring; Cellular Phone; Childhood; Code; Computer Systems; Country; Data; Detection; Development; Devices; Dimensions; Doppler Ultrasound; Fetal Development; Fetal Growth; Fetal Growth Retardation; Fetal Heart Rate; Foundations; Frontline worker; Funding; Gestational Age; Goals; Gold; Guatemala; Hand; Health; Health system; High-Risk Pregnancy; Hypertension; Image; Improve Access; Income; Infant; International; Knowledge; Label; Life; Machine Learning; Maternal Health; Maternal Mortality; Medical; Mission; Monitor; Morbidity - disease rate; National Institute of Child Health and Human Development; Neonatal Mortality; Observational Study; Outcome; Patients; Perinatal mortality demographics; Population; Pre-Eclampsia; Pregnancy; Pregnancy Complications; Prenatal care; Process; Reading; Research; Resource-limited setting; Resources; Running; Rural; Savings; Signal Transduction; System; Techniques; Technology; Time; Training; Transducers; Triage; Underserved Population; Validation; Work; base; clinical risk; cohort; cost; cost effective; cost efficient; deep learning algorithm; diagnostic accuracy; diagnostic platform; diagnostic strategy; diagnostic tool; diagnostic value; empowered; fetal; fetal medicine; field study; heart rate variability; high risk; improved; innovation; long short term memory network; low and middle-income countries; maternal hypertension; maternal risk; mortality; neonatal health; novel; novel strategies; perinatal outcomes; point of care; prediction algorithm; pregnancy disorder; prevent; prospective; public health relevance; risk prediction; screening; standard of care; stillbirth; technology validation; tool; tv watching; two-dimensional; ultrasound

PROJECT SUMMARY/ABSTRACT Life-saving advances in medical care in recent decades have reduced global mortality rates but have underperformed in addressing maternal mortality, stillbirth, and neonatal mortality. A key reason for these disparities in both low- and high-income settings is the lack of systematic screening with appropriate and affordable) technology for high priority conditions such as maternal hypertension and preeclampsia and fetal growth restriction. The development of new low-cost diagnostic tools to improve access to detection of these conditions by front-line workers would change outcomes for the most underserved populations, which is our long-term goal. In an NICHD-funded study, we collected point of care Doppler ultrasound recordings and developed a preliminary machine learning approach for detecting intrauterine growth restriction (IUGR) and maternal hypertension. The overall objective of this proposal is to prospectively validate these findings in two large underserved pregnancy cohorts in rural Guatemala and urban Georgia. Our general hypothesis is that our low-cost artificial intelligence will perform as well in detecting maternal hypertension, preeclampsia, and IUGR as standard-of-care high-cost diagnostic approaches. In Aim 1, we will validate our ultrasound-based IUGR detection algorithm against the standard of care (2-dimensional fetal imaging). In Aim 2, we will validate maternal hypertension and preeclampsia algorithms against gold-standard blood pressure devices and clinical risk prediction tools. In Aim 3, we will implement real-time versions of the algorithms validated in Aims 1 and 2 and implement them on an edge-computing system for field testing. Successful completion of this proposal will result in a novel and cost-effective approach to screening for maternal hypertension, preeclampsia, and IUGR using point-of-care Doppler connected to a low-cost, AI-enabled edge-computing system, suitable for wide use in low-resource settings. This proposal is innovative because it uses an artificial intelligence approach and widely-available point-of-care Doppler devices to provide new approaches to timely detection of high-impact maternal-fetal conditions. Our results will provide a strong basis for wide-scale deployment of new maternal and fetal screening technology which is expected to have a significant impact on maternal and fetal morbidity by improving access to timely screening. This research aligns with the NICHD's mission to advance knowledge of pregnancy, fetal development, and birth by promoting strategies that prevent maternal, infant, and childhood mortality and morbidity through lost-cost high-impact screening technology.

项目概要/摘要 近几十年来，挽救生命的医疗进步降低了全球死亡率，但 在解决孕产妇死亡率、死产和新生儿死亡率方面表现不佳。造成这些的一个关键原因是 低收入和高收入环境中的差异在于缺乏采用适当和适当的方法进行系统筛查 用于治疗孕产妇高血压、先兆子痫和胎儿等高度优先疾病的技术 生长限制。开发新的低成本诊断工具以改善这些疾病的检测 一线工作人员的条件将改变服务最匮乏人群的结果，这是我们的 长期目标。在 NICHD 资助的一项研究中，我们收集了护理点多普勒超声记录并 开发了一种初步的机器学习方法来检测宫内生长受限（IUGR） 产妇高血压。该提案的总体目标是前瞻性地在两个方面验证这些发现 危地马拉农村地区和乔治亚州城市地区有大量得不到充分服务的怀孕群体。我们的一般假设是我们的 低成本人工智能在检测孕产妇高血压、先兆子痫和 IUGR 方面表现良好 作为护理标准的高成本诊断方法。在目标 1 中，我们将验证基于超声的 IUGR 检测算法违反护理标准（二维胎儿成像）。在目标 2 中，我们将验证 针对金标准血压设备和临床的孕产妇高血压和先兆子痫算法 风险预测工具。在目标 3 中，我们将实现目标 1 和 中验证的算法的实时版本 2 并在边缘计算系统上实现它们以进行现场测试。本提案的顺利完成将 产生了一种新颖且具有成本效益的方法来筛查孕产妇高血压、先兆子痫和 IUGR 使用连接到低成本、支持人工智能的边缘计算系统的护理点多普勒，适合广泛使用 在资源匮乏的环境中。该提案具有创新性，因为它使用了人工智能方法 广泛使用的护理点多普勒设备提供及时检测高影响的新方法 母婴状况。我们的结果将为大规模部署新的孕产妇和 胎儿筛查技术预计将对母婴发病率产生重大影响 改善及时筛查的机会。这项研究符合 NICHD 的使命，即增进对 通过促进预防孕产妇、婴儿和儿童的策略来怀孕、胎儿发育和出生 通过成本损失的高影响力筛查技术降低死亡率和发病率。