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
• 负责人: Xiao Hu
• 金额: $ 52.95万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10219683
• 关键词: Address; Adherence; Adoption; Affect; Age; Algorithms; Anatomy; Biological Models; Blood Flow Velocity; Blood Pressure; Body mass index; Calibration; Cerebrovascular Circulation; Clinical; Communities; Complex; Data; Data Set; Databases; Developing Countries; Development; Devices; Electrocardiogram; Ensure; Epidemiology; Equation; Europe; European; Fibrinogen; Gender; Intracranial Hypertension; Intracranial Pressure; Lead; Learning; Libraries; Machine Learning; Measurement; Measures; Modeling; Monitor; Morphology; Movement; Nature; Patients; Physiologic pulse; Research; Residual state; Secure; Signal Transduction; Stress Tests; Surveys; System; Temporal bone structure; Testing; Training; Transcranial Doppler Ultrasonography; Ultrasonography; Validation; Variant; base; dynamic system; high risk; indexing; individual patient; kernel methods; learning algorithm; middle cerebral artery; novel; standard of care; trend

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 不存在非侵入性颅内压（NICP）评估的临床装置。过去的尝试 专注于识别非侵入性测量的ICP相关信号，但无济于事 校准问题。没有校准，只能在最好的情况下推断ICP趋势。然而， 非侵入性校准并非微不足道。通用校准将失败，因为个别患者需要不同的 校准以获得准确的结果。另一方面，将平原回归用于个性化 校准是不可行的，因为ICP不能以供从头患者开始无创。 侵入性ICP监测仍然是一个护理标准，可以利用这是不断增长的 ICP的数据库，非侵入性信号和不同的校准方程，例如，每个校准方程都是由一对侵入性构建的 数据库中的ICP和无创信号。然后，通过从一组丰富的一组中选择NICP变得可行 校准方程是从头患者的最佳选择。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​​非侵入性ICP系统的发展。这些目标 为：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对超声探针放置的变化很敏感； 3）测试提出的推广性 NICP方法。 大型流行病学调查表明，ICP仅在美国患者中仅监测ICP 指示监视。在欧洲患者中，这是一个较小的百分比（37％），而发展中的百分比更少 国家。提出的NICP方法没有与侵入性ICP相关的高风险，不需要 现场神经外科专业知识，可以在经济上部署和容易实践。因此，它 潜在影响是巨大的。