Mechanical Engineering, University of California, Berkeley, CA, USA.
Aerospace and Mechanical Engineering, Center of Informatics and Computational Science, University of Notre Dame, Notre Dame, IN, USA.
Ann Biomed Eng. 2019 Mar;47(3):714-730. doi: 10.1007/s10439-018-02191-z. Epub 2019 Jan 3.
Precise management of patients with cerebral diseases often requires intracranial pressure (ICP) monitoring, which is highly invasive and requires a specialized ICU setting. The ability to noninvasively estimate ICP is highly compelling as an alternative to, or screening for, invasive ICP measurement. Most existing approaches for noninvasive ICP estimation aim to build a regression function that maps noninvasive measurements to an ICP estimate using statistical learning techniques. These data-based approaches have met limited success, likely because the amount of training data needed is onerous for this complex applications. In this work, we discuss an alternative strategy that aims to better utilize noninvasive measurement data by leveraging mechanistic understanding of physiology. Specifically, we developed a Bayesian framework that combines a multiscale model of intracranial physiology with noninvasive measurements of cerebral blood flow using transcranial Doppler. Virtual experiments with synthetic data are conducted to verify and analyze the proposed framework. A preliminary clinical application study on two patients is also performed in which we demonstrate the ability of this method to improve ICP prediction.
精确管理脑部疾病患者通常需要颅内压（ICP）监测，这是一种高度侵入性的操作，需要在专门的 ICU 环境中进行。非侵入性 ICP 估计作为替代或筛选侵入性 ICP 测量的方法具有很大的吸引力。大多数现有的非侵入性 ICP 估计方法旨在构建一个回归函数，该函数使用统计学习技术将非侵入性测量值映射到 ICP 估计值。这些基于数据的方法取得的成功有限，可能是因为对于这种复杂的应用程序，所需的训练数据量是繁重的。在这项工作中，我们讨论了一种替代策略，旨在通过利用对生理学的机制理解来更好地利用非侵入性测量数据。具体来说，我们开发了一个贝叶斯框架，该框架将颅内生理学的多尺度模型与使用经颅多普勒的脑血流的非侵入性测量相结合。通过对合成数据进行虚拟实验来验证和分析所提出的框架。还对两名患者进行了初步的临床应用研究，证明了该方法提高 ICP 预测的能力。
