Related Experiment Video
Updated: Oct 16, 2026
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
Published on: April 13, 2013
Artificial Intelligence for Early Prediction of Intracranial Hypertension: From Single-Centre Development to
Peter Galos1, Ludvig Hult2, Giuseppe Citerio3
1Department of Medical Sciences/Neurosurgery, Uppsala University, Uppsala, Sweden. peter.galos@uu.se.
Background:
Raised intracranial pressure is one of the most severe secondary insults after traumatic brain injury. Recognising physiological patterns before they progress to dangerous intracranial pressure elevations could enable preventive rather than reactive interventions. In our previous Uppsala single-centre study, an XGBoost machine learning model showed high discriminative performance for prediction of imminent intracranial pressure insults. The model identifies short-term physiological trajectories associated with imminent threshold crossing rather than predicting independent future clinical events. Whether these results are generalisable across centres is unknown. The aim was therefore to evaluate whether the XGBoost model trained on an Uppsala dataset maintains similar performance when applied to a multicentre TBI cohort - The CENTER-TBI High-Resolution ICU cohort.
Methods:
The XGBoost model trained on the Uppsala high frequency traumatic brain injury cohort was externally validated using data from the multicentre CENTER-TBI cohort. Minute-level averages of intracranial pressure and invasive arterial blood pressure were used to predict the onset of an intracranial pressure insult within 30 min on the basis of a 4h historical window. A bidirectional validation design was applied and performance was assessed using multiple metrics.
Results:
The cohorts comprised 602 patients from Uppsala and 255 patients from the CENTER-TBI database. When trained on the Uppsala cohort and tested on CENTER-TBI, the model achieved an area under the receiver operating characteristic curve (AUROC) of 97.4%, sensitivity of 93.8%, and specificity of 98.6%. Training on CENTER-TBI and testing on the Uppsala dataset yielded an AUROC of 96.1%, sensitivity of 81.2%, and specificity of 98.7%. Balanced accuracy exceeded 88% in both validation directions.
Conclusions:
The XGBoost model retained high performance under external multicentre validation, indicating that pre-insult physiological patterns preceding increased intracranial pressure are shared across cohorts. Differences in sensitivity between validation directions were observed and may reflect cohort characteristics or dataset size. These findings support the need for local evaluation before clinical implementation.
