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Intracranial pressure based decision making: Prediction of suspected increased intracranial pressure with machine

Tadashi Miyagawa1, Minami Sasaki2, Akira Yamaura2

  • 1Department of Pediatric Neurosurgery, Matsudo City General Hospital, Matsudo, Japan.

Plos One
|October 21, 2020
PubMed

Insights

Non-invasive optic nerve sheath diameter (ONSD) measurements accurately predict increased intracranial pressure (ICP) in children. Machine learning models achieved high accuracy in identifying elevated ICP, offering a safer alternative to repeated invasive monitoring.

Area of Science:

  • Pediatric Neurosurgery
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Elevated intracranial pressure (ICP) requires monitoring, but repeated invasive procedures pose risks for children.
  • Optic nerve sheath diameter (ONSD) measurement is a promising non-invasive method for ICP assessment.
  • Limited data exists on age, brain, and ventricular parameters in normal children and AI-driven ICP prediction.

Purpose of the Study:

  • To investigate the relationship between ONSD, age, brain, and ventricular parameters in normal children.
  • To develop and validate an AI model for predicting elevated ICP in pediatric patients.
  • To establish a non-invasive method for monitoring ICP in children.

Main Methods:

  • Collected CT measurements of ONSD and other parameters from 400 normal children and 75 with suspected elevated ICP.
  • Applied supervised machine learning to predict elevated ICP based on CT measurements.
  • Analyzed correlations between ONSD, age, brain dimensions, and ventricular width.

Main Results:

  • A linear correlation was found between the natural logarithm of age and mean ONSD (mONSD) in normal children (mONSD = 0.36ln(age)+2.26).
  • mONSD correlated with brain width but not ventricular width in normal children; minimum bicaudate nuclei distance was also associated with mONSD.
  • Significant differences in mONSD and ventricular width were observed between control and elevated ICP groups.
  • Machine learning models achieved 94% training accuracy and 91% test accuracy for predicting suspected ICP.

Conclusions:

  • Mean ONSD is correlated with age and brain width, not ventricular width, in normal children.
  • Elevated ICP is associated with significant differences in mONSD and ventricular width.
  • Supervised machine learning effectively predicts suspected increased ICP in children with high accuracy.
Abstract

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