Machine learning approach for noninvasive intracranial pressure estimation using pulsatile cranial expansion waveforms.

ABSTRACT: Noninvasive methods for intracranial pressure (ICP) monitoring have emerged, but none has successfully replaced invasive techniques. This observational study developed and tested a machine learning (ML) model to estimate ICP using waveforms from a cranial extensometer device (brain4care [B4C] System). The model explored multiple waveform parameters to optimize mean ICP estimation. Data from 112 neurocritical patients with acute brain injuries were used, with 92 patients randomly assigned to training and testing, and 20 reserved for independent validation. The ML model achieved a mean absolute error of 3.00 mmHg, with a 95% confidence interval within ±7.5 mmHg. Approximately 72% of estimates from the validation sample were within 0-4 mmHg of invasive ICP values. This proof-of-concept study

SUBMITTER: Frigieri G 

PROVIDER: S-EPMC11770073 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature