Integrate Dynamic System Model and Machine Learning for Calibration-Free Noninvasive ICP

集成动态系统模型和机器学习,实现免校准无创 ICP

基本信息

  • 批准号:
    10600239
  • 负责人:
  • 金额:
    $ 53.16万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-08-01 至 2024-04-30
  • 项目状态:
    已结题

项目摘要

Project Summary No clinical device exists for noninvasive intracranial pressure (nICP) assessment. Past attempts have focused on identifying ICP-related signals that are noninvasively measureable, but have done little to address the calibration problem. Without calibration, only ICP trending can be inferred at the best. However, noninvasive calibration is not trivial. A universal calibration will fail because individual patients require different calibration to obtain accurate results. On the other hand, the use of plain regression for individualized calibration is infeasible because ICP cannot be obtained noninvasively for a de novo patient to begin with. Invasive ICP monitoring remains a standard of care and this can be leveraged to continuously grow a database of ICP, noninvasive signals, and different calibration equations, e.g., each built from a pair of invasive ICP and noninvasive signal in the database. Then nICP becomes feasible by selecting from a rich set of calibration equations the optimal choice for a de novo patient. In this project, we will pursue three aims that will lead to the development of an accurate noninvasive ICP system based on Transcranial Doppler. These aims are: 1) To implement and validate core algorithms needed for achieving accurate nICP; 2) To test if estimated nICP is sensitive to variations in ultrasound probe placement; 3) To test the generalizability of the proposed nICP approach. Large epidemiologic surveys reveal that ICP is monitored in only about 58% of US patients when ICP monitoring is indicated. It is a smaller percentage (37%) in European patients and even fewer in developing countries. The proposed nICP approach does not have the high risks associated with invasive ICP, requires no onsite neurosurgical expertise, and can be economically deployed and readily practiced. Therefore, its potential impact is enormous.
项目概要 尚无用于无创颅内压 (nICP) 评估的临床设备。过去的尝试有 专注于识别可无创测量的 ICP 相关信号,但几乎没有采取任何措施来解决 校准问题。如果没有校准,最多只能推断 ICP 趋势。然而, 无创校准并非微不足道。通用校准将会失败,因为个体患者需要不同的 校准以获得准确的结果。另一方面,使用简单回归进行个性化 校准是不可行的,因为对于新发患者来说,ICP 无法以无创方式获得。 侵入性 ICP 监测仍然是一种护理标准,可以利用它来不断发展 ICP、非侵入性信号和不同校准方程的数据库,例如,每个方程都由一对侵入性信号构建 数据库中的 ICP 和无创信号。然后,通过从丰富的集合中进行选择,nICP 变得可行 校准方程是新患者的最佳选择。在这个项目中,我们将追求三个目标 导致基于经颅多普勒的精确​​无创ICP系统的开发。这些目标 是: 1) 实现和验证实现准确 nICP 所需的核心算法; 2)测试是否估计 nICP 对超声探头放置的变化很敏感; 3)测试所提出的方法的普遍性 nICP 方法。 大型流行病学调查显示,只有约 58% 的美国患者在进行 ICP 监测时进行了 ICP 监测 已指示监控。在欧洲患者中这一比例较小(37%),在发展中国家则更少。 国家。所提出的 nICP 方法不存在与侵入性 ICP 相关的高风险,不需要 现场神经外科专业知识,可以经济地部署并易于实践。因此,其 潜在的影响是巨大的。

项目成果

期刊论文数量(11)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Morphological changes of intracranial pressure quantifies vasodilatory effect of verapamil to treat cerebral vasospasm.
  • DOI:
    10.1136/neurintsurg-2019-015499
  • 发表时间:
    2020-08
  • 期刊:
  • 影响因子:
    4.8
  • 作者:
    Liu X;Vitt JR;Hetts SW;Gudelunas K;Ho N;Ko N;Hu X
  • 通讯作者:
    Hu X
Causal relationship between neuronal activity and cerebral hemodynamics in patients with ischemic stroke.
缺血性脑卒中患者神经元活动与脑血流动力学的因果关系
  • DOI:
    10.1088/1741-2552/ab75af
  • 发表时间:
    2020-03-19
  • 期刊:
  • 影响因子:
    4
  • 作者:
    Wu D;Liu X;Gadhoumi K;Pu Y;Hemphill JC;Zhang Z;Liu L;Hu X
  • 通讯作者:
    Hu X
Patient-adaptable intracranial pressure morphology analysis using a probabilistic model-based approach.
  • DOI:
    10.1088/1361-6579/abbcbb
  • 发表时间:
    2020-11-06
  • 期刊:
  • 影响因子:
    3.2
  • 作者:
    Rashidinejad P;Hu X;Russell S
  • 通讯作者:
    Russell S
Intracranial Pressure Monitoring via External Ventricular Drain: Are We Waiting Long Enough Before Recording the Real Value?
通过心室外引流监测颅内压:我们在记录真实值之前等待的时间是否足够长?
Response to Letter to the Editor: Evaluation of a New Catheter for Simultaneous Intracranial Pressure Monitoring and Cerebral Spinal Fluid Drainage: A Pilot Study.
回复给编辑的信:评估用于同时颅内压监测和脑脊液引流的新导管:一项试点研究。
  • DOI:
    10.1007/s12028-019-00756-x
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    3.5
  • 作者:
    Liu,Xiuyun;Zimmermann,Lara;Vespa,Paul;Hu,Xiao
  • 通讯作者:
    Hu,Xiao
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Xiao Hu其他文献

Xiao Hu的其他文献

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{{ truncateString('Xiao Hu', 18)}}的其他基金

Novel Algorithm and Data Strategies to detect and Predict atrial fibrillation for post-stroke patients (NADSP)
用于检测和预测中风后患者心房颤动的新算法和数据策略 (NADSP)
  • 批准号:
    10561108
  • 财政年份:
    2023
  • 资助金额:
    $ 53.16万
  • 项目类别:
Learning to Predict Delayed Cerebral Ischemia with Novel Continuous Cerebral Arterial State Index
学习用新型连续脑动脉状态指数预测迟发性脑缺血
  • 批准号:
    10406378
  • 财政年份:
    2020
  • 资助金额:
    $ 53.16万
  • 项目类别:
Learning to Predict Delayed Cerebral Ischemia with Novel Continuous Cerebral Arterial State Index
学习用新型连续脑动脉状态指数预测迟发性脑缺血
  • 批准号:
    10599717
  • 财政年份:
    2020
  • 资助金额:
    $ 53.16万
  • 项目类别:
Integrate Dynamic System Model and Machine Learning for Calibration-Free Noninvasive ICP
集成动态系统模型和机器学习,实现免校准无创 ICP
  • 批准号:
    10219683
  • 财政年份:
    2020
  • 资助金额:
    $ 53.16万
  • 项目类别:
Learning to Predict Delayed Cerebral Ischemia with Novel Continuous Cerebral Arterial State Index
学习用新型连续脑动脉状态指数预测迟发性脑缺血
  • 批准号:
    10251348
  • 财政年份:
    2020
  • 资助金额:
    $ 53.16万
  • 项目类别:
Integrate Dynamic System Model and Machine Learning for Calibration-Free Noninvasive ICP
集成动态系统模型和机器学习,实现免校准无创 ICP
  • 批准号:
    10228768
  • 财政年份:
    2020
  • 资助金额:
    $ 53.16万
  • 项目类别:
Integrate Dynamic System Model and Machine Learning for Calibration-Free Noninvasive ICP
集成动态系统模型和机器学习,实现免校准无创 ICP
  • 批准号:
    9764511
  • 财政年份:
    2018
  • 资助金额:
    $ 53.16万
  • 项目类别:
Develop&validate SuperAlarm to detect patient deterioration with few false alarms
发展
  • 批准号:
    9268686
  • 财政年份:
    2015
  • 资助金额:
    $ 53.16万
  • 项目类别:
Develop&validate SuperAlarm to detect patient deterioration with few false alarms
发展
  • 批准号:
    8943567
  • 财政年份:
    2015
  • 资助金额:
    $ 53.16万
  • 项目类别:
ICP Elevation Alerting Based on a Predictive Model Hosting Platform
基于预测模型托管平台的 ICP 海拔警报
  • 批准号:
    8732711
  • 财政年份:
    2012
  • 资助金额:
    $ 53.16万
  • 项目类别:

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