Abstract

OBJECTIVE

Accurate noninvasive estimation of intracranial pressure (ICP) remains a major challenge in critical care. We developed a machine learning algorithm that integrates system identification and rankingconstrained optimization to estimate mean ICP from noninvasive signals.

METHODS

A machine learning framework was proposed to obtain estimated mean ICP values using arbitrary noninvasive signals. The subspace system identification algorithm is employed to identify cerebral hemodynamics models for ICP simulation using arterial blood pressure (ABP), cerebral blood velocity (CBv), and R-wave to R-wave interval (R-R interval) signals in a comprehensive database. A mapping function to describe the relationship between the features of noninvasive signals and the estimation errors is learned using innovative ranking constraints through convex optimization. Patients across multiple clinical settings were randomly split into testing and training datasets for performance evaluation of the mapping function.

RESULTS

The results indicate that about 31.88% of testing entries achieved estimation errors within 2 mmHg and 34.07% of testing entries between 2 mmHg and 6 mmHg from the nonlinear mapping with constraints.

CONCLUSION

Our results demonstrate the feasibility of the proposed noninvasive ICP estimation approach.

SIGNIFICANCE

This work presents a proof-of-concept framework for noninvasive ICP estimation in patients with acute brain injury and related conditions. Although the findings demonstrate the feasibility of the proposed framework, important limitations remain, particularly regarding estimation accuracy, cohort diversity, and validation across clinical settings. Additional algorithm development, expanded multicenter datasets, and rigorous prospective validation are therefore required to improve performance and establish clinical utility.

MeSH

Research Design, Blood, Intracranial Pressure, Algorithms, Machine Learning, Work, Learning, Machine Learning Algorithms