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:
    10228768
  • 负责人:
  • 金额:
    $ 54.42万
  • 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 2021-12-31
  • 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.
项目摘要 无创颅内压(nICP)评估尚无临床器械。过去的尝试 专注于识别ICP相关的信号,这些信号是非侵入性测量的,但几乎没有解决 校准问题。在没有校准的情况下,最好只能推断出ICP趋势。然而,在这方面, 非侵入性校准不是微不足道的。通用校准将失败,因为个别患者需要不同的校准。 校准以获得准确的结果。另一方面,使用平原回归进行个性化 校准是不可行的,因为对于从头开始开始的患者不能无创地获得ICP。 有创ICP监测仍然是一种标准护理,可以利用这一点来不断增加 ICP数据库、无创信号和不同的校准方程,例如,每一个都是由一对侵入性的 数据库里有颅内压和无创信号。然后,通过从丰富的 校准方程是初治患者的最佳选择。在这个项目中,我们将追求三个目标, 导致了基于经颅多普勒的精确的无创ICP系统的发展。这些目标 是:1)实施和验证实现准确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敏感; 3)为了测试所提出的 nICP方法。 大型流行病学调查显示,当ICP时,仅在约58%的美国患者中监测ICP 监测显示。在欧洲患者中,这一比例较小(37%),在发展中国家甚至更少。 国家所提出的nICP方法不具有与侵入性ICP相关的高风险,不需要 现场神经外科专业知识,并且可以经济地部署和易于实践。因此其 潜在的影响是巨大的。

项目成果

期刊论文数量(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 }}

Xiao Hu其他文献

Xiao Hu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('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:
    $ 54.42万
  • 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:
    10600239
  • 财政年份:
    2020
  • 资助金额:
    $ 54.42万
  • 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:
    $ 54.42万
  • 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:
    $ 54.42万
  • 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:
    $ 54.42万
  • 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:
    $ 54.42万
  • 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:
    $ 54.42万
  • 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:
    $ 54.42万
  • 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:
    $ 54.42万
  • 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:
    $ 54.42万
  • 项目类别:

相似海外基金

An innovative, AI-driven prehabilitation platform that increases adherence, enhances post-treatment outcomes by at least 50%, and provides cost savings of 95%.
%20创新、%20AI驱动%20康复%20平台%20%20增加%20依从性、%20增强%20治疗后%20结果%20by%20at%20至少%2050%、%20和%20提供%20成本%20节省%20of%2095%
  • 批准号:
    10057526
  • 财政年份:
    2023
  • 资助金额:
    $ 54.42万
  • 项目类别:
    Grant for R&D
Improving Repositioning Adherence in Home Care: Supporting Pressure Injury Care and Prevention
提高家庭护理中的重新定位依从性:支持压力损伤护理和预防
  • 批准号:
    490105
  • 财政年份:
    2023
  • 资助金额:
    $ 54.42万
  • 项目类别:
    Operating Grants
I-Corps: Medication Adherence System
I-Corps:药物依从性系统
  • 批准号:
    2325465
  • 财政年份:
    2023
  • 资助金额:
    $ 54.42万
  • 项目类别:
    Standard Grant
Unintrusive Pediatric Logging Orthotic Adherence Device: UPLOAD
非侵入式儿科记录矫形器粘附装置:上传
  • 批准号:
    10821172
  • 财政年份:
    2023
  • 资助金额:
    $ 54.42万
  • 项目类别:
Nuestro Sueno: Cultural Adaptation of a Couples Intervention to Improve PAP Adherence and Sleep Health Among Latino Couples with Implications for Alzheimer’s Disease Risk
Nuestro Sueno:夫妻干预措施的文化适应,以改善拉丁裔夫妇的 PAP 依从性和睡眠健康,对阿尔茨海默病风险产生影响
  • 批准号:
    10766947
  • 财政年份:
    2023
  • 资助金额:
    $ 54.42万
  • 项目类别:
CO-LEADER: Intervention to Improve Patient-Provider Communication and Medication Adherence among Patients with Systemic Lupus Erythematosus
共同领导者:改善系统性红斑狼疮患者的医患沟通和药物依从性的干预措施
  • 批准号:
    10772887
  • 财政年份:
    2023
  • 资助金额:
    $ 54.42万
  • 项目类别:
Pharmacy-led Transitions of Care Intervention to Address System-Level Barriers and Improve Medication Adherence in Socioeconomically Disadvantaged Populations
药房主导的护理干预转型,以解决系统层面的障碍并提高社会经济弱势群体的药物依从性
  • 批准号:
    10594350
  • 财政年份:
    2023
  • 资助金额:
    $ 54.42万
  • 项目类别:
Antiretroviral therapy adherence and exploratory proteomics in virally suppressed people with HIV and stroke
病毒抑制的艾滋病毒和中风患者的抗逆转录病毒治疗依从性和探索性蛋白质组学
  • 批准号:
    10748465
  • 财政年份:
    2023
  • 资助金额:
    $ 54.42万
  • 项目类别:
Improving medication adherence and disease control for patients with multimorbidity: the role of price transparency tools
提高多病患者的药物依从性和疾病控制:价格透明度工具的作用
  • 批准号:
    10591441
  • 财政年份:
    2023
  • 资助金额:
    $ 54.42万
  • 项目类别:
Development and implementation of peer-facilitated decision-making and referral support to increase uptake and adherence to HIV pre-exposure prophylaxis in African Caribbean and Black communities in Ontario
制定和实施同行协助决策和转介支持,以提高非洲加勒比地区和安大略省黑人社区对艾滋病毒暴露前预防的接受和依从性
  • 批准号:
    491109
  • 财政年份:
    2023
  • 资助金额:
    $ 54.42万
  • 项目类别:
    Fellowship Programs
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了