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Related Concept Videos

Increased Intracranial Pressure l: Introduction01:14

Increased Intracranial Pressure l: Introduction

Intracranial hypertension is a sustained elevation of intracranial pressure (ICP) above 22 mm Hg. In supine adults, normal ICP is ~7–15 mm Hg.The rigid, nonexpandable cranium contains three components—brain tissue, blood, and cerebrospinal fluid (CSF)—that total ~1,700 mL in a typical adult: 1,400 mL brain (~80%), 150 mL blood (~10%), and 150 mL CSF (~10%). According to the Monro–Kellie doctrine, total intracranial volume is effectively fixed. When one component expands, CSF and venous blood...
Increased Intracranial Pressure ll: Pathophysiology01:29

Increased Intracranial Pressure ll: Pathophysiology

Increased intracranial pressure (ICP) refers to a potentially life-threatening rise in pressure inside the skull. This usually happens when there is a major change in the volume of brain tissue, blood, or cerebrospinal fluid (CSF) — the three components inside the skull. According to the Monro-Kellie doctrine, if the volume of one component increases, the volumes of the other components must decrease to maintain normal pressure. If this does not happen, ICP rises.The process often begins with...

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Related Experiment Video

Updated: May 7, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Machine learning approach for noninvasive intracranial pressure estimation using pulsatile cranial expansion

Gustavo Frigieri1,2, Sérgio Brasil2, Danilo Cardim1

  • 1brain4care, Johns Creek, GA, USA.

NPJ Digital Medicine
|January 26, 2025
PubMed
Summary
This summary is machine-generated.

A new machine learning model estimates intracranial pressure (ICP) noninvasively using brain4care (B4C) system waveforms. This proof-of-concept study shows feasibility, paving the way for improved patient monitoring.

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Artificial Intelligence in Medicine

Background:

  • Intracranial pressure (ICP) monitoring is crucial for neurocritical patients.
  • Current invasive ICP monitoring methods carry risks and limitations.
  • Noninvasive ICP monitoring remains a significant unmet clinical need.

Purpose of the Study:

  • To develop and validate a machine learning (ML) model for noninvasive ICP estimation.
  • To utilize waveform data from the brain4care (B4C) System for ICP prediction.
  • To assess the feasibility of ML-driven noninvasive ICP monitoring.

Main Methods:

  • An observational study involving 112 neurocritical patients with acute brain injuries.
  • Development of an ML model using waveform parameters from the B4C System.
  • Randomized training/testing (92 patients) and independent validation (20 patients).

Main Results:

  • The ML model achieved a mean absolute error of 3.00 mmHg for ICP estimation.
  • A 95% confidence interval for the estimates was within ±7.5 mmHg.
  • 72% of validation estimates were within 0-4 mmHg of invasive ICP values.

Conclusions:

  • Noninvasive ICP estimation using the B4C System and ML is feasible.
  • This study serves as a proof-of-concept for a novel monitoring approach.
  • Further prospective studies are required to confirm clinical utility in diverse settings.