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：
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
项目类别：
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

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，每个方程均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。