Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Comparing the Survival Analysis of Two or More Groups

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Reevaluating the Effectiveness of Intracranial Pressure Monitoring in Severe Traumatic Brain Injury: Influence of Withdrawal of Life-Sustaining Treatments.

Neurosurgery·2026

Same author

Method of Removal of Aquatic Pond Weed Variably Impacts Presence of Potentially Toxic Cyanobacteria.

microPublication biology·2026

Same author

Commentary: Craniometric Anatomy of the Middle Meningeal Artery Bifurcation With Relevance for Simultaneous Surgical Occlusion in Chronic Subdural Hematoma.

Operative neurosurgery (Hagerstown, Md.)·2026

Same author

Comparative effectiveness of antidepressants for depression using EHRs from two health systems.

BMC psychiatry·2026

Same author

Reframing neurotrauma research for LSCO: lessons from Ukraine for the UK Defence Medical Services.

BMJ military health·2026

Same author

A meta-analysis suggests that TMS targeting the hippocampal network selectively improves episodic memory.

eLife·2026

Same journal

Urinary Tract Infection Exaggerates Cognitive Deficits and Region-Specific Neuroinflammation Following Traumatic Brain Injury.

Journal of neurotrauma·2026

Same journal

Fall Risk and Physical/Occupational Therapy Referral Patterns in Older Adults with Mild Traumatic Brain Injury.

Journal of neurotrauma·2026

Same journal

The Effects of High-Thoracic Spinal Cord Injury on the Heart Transcriptome.

Journal of neurotrauma·2026

Same journal

Rigor and Transparency in Two Neurotrauma-Publishing Journals: Editorial Policies Improve Transparent Reporting.

Journal of neurotrauma·2026

Same journal

Neuroimaging and Fluid-Based Biomarkers in Sport-Related Concussion in Female Athletes: A Scoping Review.

Journal of neurotrauma·2026

Same journal

Transcranial Photobiomodulation Promotes Neurological Resilience in Current Collegiate American Football Players Exposed to Repetitive Head Acceleration Events.

Journal of neurotrauma·2026

See all related articles

Search research articles

Home
Ai-aided Triage For Gswh: Validating An Interpretable Hct-based Mortality Model.

Home
Ai-aided Triage For Gswh: Validating An Interpretable Hct-based Mortality Model.

Related Experiment Video

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

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

View abstract on PubMed

Summary

This summary is machine-generated.

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 model penetrating traumatic brain injury

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Related Experiment Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

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.