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:
    10567313
  • 负责人:
  • 金额:
    $ 65.53万
  • 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/文摘

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:
    $ 65.53万
  • 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:
    10708135
  • 财政年份:
    2022
  • 资助金额:
    $ 65.53万
  • 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:
    $ 65.53万
  • 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:
    $ 65.53万
  • 项目类别:

相似海外基金

CAREER: CAS-Climate: Forecast-informed Flexible Reservoir System Modeling Enabled by Artificial Intelligence Algorithms Using Subseasonal-to-Seasonal Hydroclimatological Forecasts
职业:CAS-气候:利用次季节到季节水文气候预测的人工智能算法实现基于预测的灵活水库系统建模
  • 批准号:
    2236926
  • 财政年份:
    2023
  • 资助金额:
    $ 65.53万
  • 项目类别:
    Continuing Grant
Artificial intelligence algorithms to predict risk of injury in racehorses.
预测赛马受伤风险的人工智能算法。
  • 批准号:
    LP210200798
  • 财政年份:
    2023
  • 资助金额:
    $ 65.53万
  • 项目类别:
    Linkage Projects
Collaborative Research: SHF: Small: Artificial Intelligence of Things (AIoT): Theory, Architecture, and Algorithms
合作研究:SHF:小型:物联网人工智能 (AIoT):理论、架构和算法
  • 批准号:
    2221742
  • 财政年份:
    2022
  • 资助金额:
    $ 65.53万
  • 项目类别:
    Standard Grant
Performance-Based Earthquake Engineering 2.0: Machine-Learning and Artificial Intelligence Algorithms for seismic hazard and vulnerability.
基于性能的地震工程 2.0:地震灾害和脆弱性的机器学习和人工智能算法。
  • 批准号:
    2765246
  • 财政年份:
    2022
  • 资助金额:
    $ 65.53万
  • 项目类别:
    Studentship
The 'risk of risk': remodelling artificial intelligence algorithms for predicting child abuse.
“风险中的风险”:重塑人工智能算法以预测虐待儿童行为。
  • 批准号:
    ES/R00983X/2
  • 财政年份:
    2022
  • 资助金额:
    $ 65.53万
  • 项目类别:
    Research Grant
Collaborative Research: SHF: Small: Artificial Intelligence of Things (AIoT): Theory, Architecture, and Algorithms
合作研究:SHF:小型:物联网人工智能 (AIoT):理论、架构和算法
  • 批准号:
    2221741
  • 财政年份:
    2022
  • 资助金额:
    $ 65.53万
  • 项目类别:
    Standard Grant
Developing a platform for deep phenotyping of heart failure with preserved ejection fraction using raw, widely-available, multi-modality data and artificial intelligence algorithms
使用原始、广泛可用的多模态数据和人工智能算法,开发一个对射血分数保留的心力衰竭进行深度表型分析的平台
  • 批准号:
    10683803
  • 财政年份:
    2022
  • 资助金额:
    $ 65.53万
  • 项目类别:
Early-assymptomatic-dementia prediction based on a white-matter biomarker using Artificial Intelligence algorithms
使用人工智能算法基于白质生物标志物的早期无症状痴呆症预测
  • 批准号:
    460558
  • 财政年份:
    2022
  • 资助金额:
    $ 65.53万
  • 项目类别:
Concluding 50 Years of Research in Wireless Communications: Algorithms for Artificial Intelligence and Optimization in Networks Beyond 5G and Thereafter
总结无线通信 50 年的研究:5G 及以后网络中的人工智能和优化算法
  • 批准号:
    RGPIN-2022-04417
  • 财政年份:
    2022
  • 资助金额:
    $ 65.53万
  • 项目类别:
    Discovery Grants Program - Individual
De novo development of small CRISPR-Cas proteins using artificial intelligence algorithms
使用人工智能算法从头开发小型 CRISPR-Cas 蛋白
  • 批准号:
    10544772
  • 财政年份:
    2022
  • 资助金额:
    $ 65.53万
  • 项目类别:
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了