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，医疗保健的挽救生命进步降低了全球死亡率，但已有 在解决孕产妇死亡率，死产和新生儿死亡率方面的表现不佳。这些关键原因 低收入和高收入设置的差异是缺乏适当的系统筛查 负担得起的）技术，用于高优先级条件，例如母体高血压和先兆子痫和胎儿 生长限制。开发新的低成本诊断工具，以改善检测的访问 一线工人的条件将改变服务最不足的人群的结果，这是我们的 长期目标。在NICHD资助的研究中，我们收集了护理点多普勒超声录音和 开发了一种用于检测宫内生长限制（IUGR）和 母亲高血压。该提案的总体目的是前瞻性地验证这些发现 在危地马拉农村和佐治亚州农村地区，服务不足的怀孕人群。我们的总体假设是我们 低成本人工智能将在检测母体高血压，先兆子痫和IUGR方面表现出色 作为护理标准的高成本诊断方法。在AIM 1中，我们将验证基于超声的IUGR 针对护理标准（二维胎儿成像）的检测算法。在AIM 2中，我们将验证 针对金标准血压装置和临床的母体高血压和先兆子痫算法 风险预测工具。在AIM 3中，我们将实施AIMS 1和AIMS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它们。该提案的成功完成将 导致一种新颖且具有成本效益的方法来筛查母体高血压，先兆子痫和IUGR 使用连接到低成本，启用AI的边缘计算系统的即时的多普勒，适用于广泛使用 在低资源设置中。该提案具有创新性，因为它使用人工智能方法和 广泛可用的保健多普勒设备，以提供及时检测的新方法 母亲条件。我们的结果将为大规模部署新的孕产妇和 胎儿筛查技术预计会对孕产妇和胎儿发病产生重大影响 改善及时筛选的访问。这项研究符合NICHD的使命，以提高知识 通过促进预防母亲，婴儿和童年的策略，怀孕，胎儿发育和出生 通过丢失成本的高影响力筛查技术，死亡率和发病率。