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AI-Aided Triage for GSWH: Validating an Interpretable HCT-Based Mortality Model.

Ali Mansour1, Jordan Fuhrman2, Ronald Alvarado-Dyer3

  • 1Department of Neurology, Division of Neurocritical Care, University of Chicago Medical Center, Chicago, Illinois, USA.

Journal of Neurotrauma
|March 10, 2026
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Summary

A new artificial intelligence model uses head CT scans to predict mortality in civilian gunshot wounds to the head (GSWH). This interpretable tool offers objective risk estimates, aiding in early prognostication and triage for penetrating brain injuries.

Keywords:
machine-learning modelpenetrating traumatic brain injury

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Area of Science:

  • Neurosurgery
  • Radiology
  • Artificial Intelligence

Background:

  • Civilian gunshot wounds to the head (GSWH) have high mortality rates.
  • Current triage lacks standardized, imaging-based tools for GSWH.
  • Initial head computerized tomography (HCT) scans are standard but underutilized for prognostication.

Purpose of the Study:

  • To develop and evaluate an interpretable, attention-based multiple-instance learning (MIL) model.
  • To predict in-hospital mortality using initial HCT scans in GSWH patients.
  • To provide objective, reproducible risk estimates and case-level explanations for penetrating brain injury.

Main Methods:

  • Retrospective cohort study of 222 adult GSWH patients.
  • Development and validation of an attention-based MIL model using stratified random splits.
  • Evaluation of model performance on an independent test set (n=54) with 100-fold cross-validation on the development set.

Main Results:

  • The MIL model achieved high discrimination for mortality (AUC: 0.92) on the test set.
  • Sensitivity was 0.88 and specificity was 0.87 at the optimal operating point.
  • Attention maps highlighted critical neuroanatomical regions (brainstem, deep midline, ventricles) correlating with mortality.

Conclusions:

  • An interpretable, HCT-based MIL model can accurately predict mortality in GSWH.
  • The model provides objective, reproducible risk stratification and transparent explanations.
  • This tool supports early prognostication and an imaging-first triage strategy for penetrating brain injury.