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
- 来源:
- 关键词:AddressAdherenceAdoptionAffectAgeAlgorithmsAnatomyBiological ModelsBlood Flow VelocityBlood PressureBody mass indexCalibrationCerebrovascular CirculationClinicalCommunitiesComplexDataData SetDatabasesDeveloping CountriesDevelopmentDevicesElectrocardiogramEnsureEpidemiologyEquationEuropeEuropeanFibrinogenGenderIntracranial HypertensionIntracranial PressureLeadLearningLibrariesMachine LearningMeasurementMeasuresModelingMonitorMorphologyMovementNaturePatientsPhysiologic pulseResearchResidual stateSecureSignal TransductionStress TestsSurveysSystemTemporal bone structureTestingTrainingTranscranial Doppler UltrasonographyUltrasonographyValidationVariantbasedynamic systemhigh riskindexingindividual patientkernel methodslearning algorithmmiddle cerebral arterynovelstandard of caretrend
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.
项目总结
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)
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Xiao Hu其他文献
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{{ truncateString('Xiao Hu', 18)}}的其他基金
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- 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
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10600239 - 财政年份:2020
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Learning to Predict Delayed Cerebral Ischemia with Novel Continuous Cerebral Arterial State Index
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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:
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- 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
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$ 54.42万 - 项目类别:
Develop&validate SuperAlarm to detect patient deterioration with few false alarms
发展
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