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

基本信息

  • 批准号:
    10708135
  • 负责人:
  • 金额:
    $ 61.92万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 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

项目摘要

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/摘要 近几十年来,医疗保健方面的救生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问题方面表现不佳。一个关键原因是, 低收入和高收入环境中的差异是缺乏适当和 负担得起的)技术,用于高优先级的条件,如产妇高血压和先兆子痫和胎儿 增长限制。开发新的低成本诊断工具,以改善这些疾病的检测 一线工人的工作条件将改变最缺乏服务的人群的结果,这是我们的 长期目标。在NICHD资助的一项研究中,我们收集了床旁多普勒超声记录, 开发了一种初步的机器学习方法来检测宫内生长受限(IUGR), 母亲高血压本提案的总体目标是在两个方面前瞻性地验证这些发现, 在危地马拉农村和格鲁吉亚城市,有大量未得到充分服务的怀孕妇女。我们的一般假设是, 低成本的人工智能将在检测孕产妇高血压、先兆子痫和IUGR方面表现良好 作为高成本诊断方法的标准护理。在目标1中,我们将验证我们基于超声的IUGR 检测算法与护理标准(二维胎儿成像)。在目标2中,我们将验证 针对金标准血压装置的孕产妇高血压和先兆子痫算法和临床 风险预测工具。在目标3中,我们将实现目标1中验证的算法的实时版本, 2并在边缘计算系统上实现,进行现场测试。成功完成此提案将 为筛查母体高血压、先兆子痫和IUGR提供了一种新的、具有成本效益的方法 使用连接到低成本、支持AI的边缘计算系统的即时多普勒,适合广泛使用 在低资源环境中。这一提议具有创新性,因为它使用了人工智能方法, 广泛使用的床旁多普勒设备,为及时检测高冲击提供新的方法 母胎情况我们的研究结果将为大规模部署新的孕产妇和新生儿保健服务提供强有力的基础。 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, 改善及时筛查的机会。这项研究符合NICHD的使命,以提高对 通过促进预防孕产妇、婴儿和儿童疾病的战略, 通过低成本高影响筛查技术降低死亡率和发病率。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Gari David Clifford其他文献

Gari David Clifford的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Gari David Clifford', 18)}}的其他基金

Artificial Intelligence Applied to Video and Speech for Objectively Evaluating Social Interaction and Depression in Mild Cognitive Impairment
人工智能应用于视频和语音,客观评估轻度认知障碍患者的社交互动和抑郁情况
  • 批准号:
    10810965
  • 财政年份:
    2023
  • 资助金额:
    $ 61.92万
  • 项目类别:
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
  • 财政年份:
    2022
  • 资助金额:
    $ 61.92万
  • 项目类别:
Methods and Tools for Integrating Pathomics Data into Cancer Registries
将病理组学数据整合到癌症登记处的方法和工具
  • 批准号:
    10247096
  • 财政年份:
    2018
  • 资助金额:
    $ 61.92万
  • 项目类别:
Methods and Tools for Integrating Pathomics Data into Cancer Registries
将病理组学数据整合到癌症登记处的方法和工具
  • 批准号:
    10405657
  • 财政年份:
    2018
  • 资助金额:
    $ 61.92万
  • 项目类别:

相似海外基金

Rational design of rapidly translatable, highly antigenic and novel recombinant immunogens to address deficiencies of current snakebite treatments
合理设计可快速翻译、高抗原性和新型重组免疫原,以解决当前蛇咬伤治疗的缺陷
  • 批准号:
    MR/S03398X/2
  • 财政年份:
    2024
  • 资助金额:
    $ 61.92万
  • 项目类别:
    Fellowship
Re-thinking drug nanocrystals as highly loaded vectors to address key unmet therapeutic challenges
重新思考药物纳米晶体作为高负载载体以解决关键的未满足的治疗挑战
  • 批准号:
    EP/Y001486/1
  • 财政年份:
    2024
  • 资助金额:
    $ 61.92万
  • 项目类别:
    Research Grant
CAREER: FEAST (Food Ecosystems And circularity for Sustainable Transformation) framework to address Hidden Hunger
职业:FEAST(食品生态系统和可持续转型循环)框架解决隐性饥饿
  • 批准号:
    2338423
  • 财政年份:
    2024
  • 资助金额:
    $ 61.92万
  • 项目类别:
    Continuing Grant
Metrology to address ion suppression in multimodal mass spectrometry imaging with application in oncology
计量学解决多模态质谱成像中的离子抑制问题及其在肿瘤学中的应用
  • 批准号:
    MR/X03657X/1
  • 财政年份:
    2024
  • 资助金额:
    $ 61.92万
  • 项目类别:
    Fellowship
CRII: SHF: A Novel Address Translation Architecture for Virtualized Clouds
CRII:SHF:一种用于虚拟化云的新型地址转换架构
  • 批准号:
    2348066
  • 财政年份:
    2024
  • 资助金额:
    $ 61.92万
  • 项目类别:
    Standard Grant
The Abundance Project: Enhancing Cultural & Green Inclusion in Social Prescribing in Southwest London to Address Ethnic Inequalities in Mental Health
丰富项目:增强文化
  • 批准号:
    AH/Z505481/1
  • 财政年份:
    2024
  • 资助金额:
    $ 61.92万
  • 项目类别:
    Research Grant
ERAMET - Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
ERAMET - 快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
  • 批准号:
    10107647
  • 财政年份:
    2024
  • 资助金额:
    $ 61.92万
  • 项目类别:
    EU-Funded
BIORETS: Convergence Research Experiences for Teachers in Synthetic and Systems Biology to Address Challenges in Food, Health, Energy, and Environment
BIORETS:合成和系统生物学教师的融合研究经验,以应对食品、健康、能源和环境方面的挑战
  • 批准号:
    2341402
  • 财政年份:
    2024
  • 资助金额:
    $ 61.92万
  • 项目类别:
    Standard Grant
Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
  • 批准号:
    10106221
  • 财政年份:
    2024
  • 资助金额:
    $ 61.92万
  • 项目类别:
    EU-Funded
Recite: Building Research by Communities to Address Inequities through Expression
背诵:社区开展研究,通过表达解决不平等问题
  • 批准号:
    AH/Z505341/1
  • 财政年份:
    2024
  • 资助金额:
    $ 61.92万
  • 项目类别:
    Research Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了