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: 10708135
• 负责人: Gari David Clifford
• 金额: $ 61.92万
• 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/10708135
• 关键词: Acceleration; Address; Algorithms; Artificial Intelligence; Birth; Blood Pressure; Caring; Cellular Phone; Childhood; Code; Computer Systems; Country; Data; Detection; Development; Devices; Disparity; Doppler Ultrasound; Fetal Development; Fetal Growth; Fetal Growth Retardation; Fetal Heart Rate; Foundations; Frontline worker; Funding; Gestational Age; Goals; Guatemala; 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; Research; Resource-limited setting; Resources; Running; Rural; Signal Transduction; System; Techniques; Technology; Time; Training; Transducers; Triage; Underserved Population; Validation; Work; clinical risk; cohort; cost; cost effective; data exchange; deep learning algorithm; diagnostic accuracy; diagnostic platform; diagnostic strategy; diagnostic tool; diagnostic value; empowerment; fetal; fetal medicine; field study; heart rate variability; high risk; improved; innovation; long short term memory; low and middle-income countries; maternal hypertension; maternal risk; mortality; neonatal health; neural network; novel; novel strategies; perinatal outcomes; point of care; predictive tools; pregnancy disorder; prevent; prospective; public health relevance; risk prediction model; screening; standard of care; stillbirth; technology validation; 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.

项目摘要/摘要 近几十年来，医疗保健方面拯救生命的进步降低了全球死亡率，但 在解决产妇死亡率、死产和新生儿死亡率方面表现不佳。造成这些问题的一个关键原因 低收入和高收入环境中的差异是缺乏系统的筛查和适当的 负担得起)针对母亲高血压、先兆子痫和胎儿等高优先级疾病的技术 生长限制。开发新的低成本诊断工具，以改善对这些疾病的检测 一线工人的条件将改变服务最少的人群的结果，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和 开发了一种初步的机器学习方法来检测胎儿宫内生长受限(IUGR)和 母体高血压。这项提案的总体目标是前瞻性地在两个方面验证这些发现 危地马拉农村和佐治亚州城市的大量未得到充分服务的怀孕队列。我们的普遍假设是，我们的 低成本的人工智能将在检测母亲高血压、先兆子痫和宫内发育迟缓方面发挥同样的作用 作为标准护理的高成本诊断方法。在目标1中，我们将验证我们基于超声的IUGR 检测算法对照标准护理(二维胎儿成像)。在目标2中，我们将验证 针对金标准血压装置的母体高血压和先兆子痫算法及临床 风险预测工具。在目标3中，我们将实现在目标1和目标1中验证的算法的实时版本 2，并在用于现场测试的边缘计算系统上实现。成功完成这项提案将 为筛查母亲高血压、先兆子痫和宫内发育迟缓提供了一种新的、经济有效的方法 使用医疗点多普勒连接到低成本、支持人工智能的边缘计算系统，适合广泛使用 在资源不足的环境中。这一提议具有创新性，因为它使用了人工智能方法，并 广泛可用的护理点多普勒设备，为及时检测高冲击性疾病提供新的方法 母婴状况。我们的结果将为广泛部署新的孕产妇和 胎儿筛查技术预计将对孕产妇和胎儿发病率产生重大影响 改善获得及时筛查的机会。这项研究与NICHD的使命相一致，即促进对 通过促进预防母婴和儿童出生的策略，促进怀孕、胎儿发育和生育 通过低成本、高影响力的筛查技术降低死亡率和发病率。