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.

项目总结/文摘