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Related Experiment Video

Updated: Sep 11, 2025

A Pediatric Concussion Model in Mice: Closed Head Injury with Long-Term Disorders (CHILD)

A Pediatric Concussion Model in Mice: Closed Head Injury with Long-Term Disorders (CHILD)

Published on: February 7, 2025

Outcome Prediction in Pediatric Traumatic Brain Injury Utilizing Social Determinants of Health and Machine Learning

Artem Kaliaev¹, Maryam Vejdani-Jahromi², Adrian Gunawan³

¹From the Radiology Department of Boston University Medical Center (A.K., M.Q., M.A., B.N.S., C.F., A.M.), Boston, Massachusetts artem.kaliaev@bmc.org.

AJNR. American Journal of Neuroradiology

|August 11, 2025

Summary

Social determinants of health significantly impact pediatric traumatic brain injury (TBI) outcomes. A machine learning algorithm using factors like age and insurance type accurately predicts head CT findings, aiding clinical decisions.

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Last Updated: Sep 11, 2025

A Pediatric Concussion Model in Mice: Closed Head Injury with Long-Term Disorders (CHILD)

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Published on: February 7, 2025

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury

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Published on: May 27, 2022

Murine Model of Controlled Cortical Impact for the Induction of Traumatic Brain Injury

Murine Model of Controlled Cortical Impact for the Induction of Traumatic Brain Injury

Published on: August 16, 2019

Area of Science:

Pediatric Traumatic Brain Injury (TBI) research
Medical Machine Learning Applications
Public Health and Health Disparities

Background:

Socioeconomic disparities significantly affect pediatric patients with traumatic brain injury (TBI).
Understanding social determinants of health is crucial for improving TBI outcomes.
Existing research highlights disparities in healthcare access and outcomes for pediatric TBI.

Purpose of the Study:

To analyze the impact of social determinants of health on pediatric head injury outcomes.
To develop a novel machine learning algorithm (MLA) for predicting head computed tomography (CT) findings in pediatric TBI.
To incorporate socioeconomic factors into predictive models for pediatric head CT results.

Main Methods:

Retrospective cohort study of 211 pediatric blunt trauma patients at a New England safety net hospital (2006-2013).
Collected socioeconomic data (race, language, income, insurance) and clinical data (ISS, age, sex, mechanism).
Trained and evaluated 22 multi-parametric MLAs using stratified sampling, 5-fold cross-validation, and hyperparameter tuning.

Main Results:

Injury Severity Score (ISS), age, and insurance type were significant predictors of head CT outcomes (p<0.05).
Children under 5 years old had a higher median age (1.8 years) with positive head CT findings compared to those without (9.1 years).
A Fine Gaussian Support Vector Machine (SVM) model achieved the highest test Area Under the Curve (AUC) of 0.923 with an accuracy of 0.837.

Conclusions:

Key predictors for clinically relevant head CT findings in pediatric TBI include ISS, age, and social determinants of health.
Children under five years old are identified as a higher-risk group for significant head CT findings.
A novel Fine Gaussian SVM model demonstrates high accuracy in predicting pediatric TBI outcomes, potentially improving clinical decision-making and reducing radiation exposure.