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
• 负责人: Xiao Hu
• 金额: $ 54.42万
• 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 2021-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10228768
• 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 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。