Data-augmented modeling of intracranial pressure

TL;DR

This paper presents a Bayesian framework that integrates physiological models with noninvasive measurements to improve intracranial pressure estimation, potentially reducing reliance on invasive monitoring.

Contribution

It introduces a novel data-physiology integrated Bayesian approach for noninvasive ICP estimation, leveraging mechanistic understanding rather than solely statistical learning.

Findings

Verified framework with synthetic data

Demonstrated improved ICP prediction in two clinical cases

Highlights potential for noninvasive ICP monitoring

Abstract

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.