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Data-driven standards for infant skull thickness distributions in computational modeling and analysis
Yousef Alsanea1, Tagrid M Ruiz-Maldonado2, Brittany Coats1
1Department of Mechanical Engineering, University of Utah, Salt Lake City, Utah, USA.
Insights
This study quantifies infant skull thickness, creating data-driven standards to improve computational models for predicting head trauma injuries and preventing child abuse. These findings offer crucial anatomical guidelines for infant injury risk assessment.
Area of Science:
- Biomechanical Engineering
- Pediatric Traumatology
- Computational Anatomy
Background:
- Child abuse is a critical global issue, with infants under one year facing the highest fatality risk.
- Computational modeling aids in predicting injury and validating histories to prevent abuse, but lacks anatomical variability data.
- Accurate injury prediction requires understanding natural anatomical variations in populations.
Purpose of the Study:
- To quantify skull thickness distributions in infants to establish data-driven anatomical standards.
- To enhance the accuracy of computational models for predicting head trauma injuries in infants.
- To provide age- and sex-based guidelines for injury prediction models.
Main Methods:
- Quantified skull thickness in 266 infants, analyzing age and head circumference as predictors.
- Categorized infants under 12 months into four age groups using thickness distribution breaks and variance optimization.
- Assessed sex differences and lateral symmetry in skull thickness across developmental stages.
Main Results:
- Head circumference was a better predictor of skull thickness than age for infants under 2.5 months.
- Identified four distinct age-based skull thickness categories for infants under 12 months.
- Found no significant average sex differences in skull thickness, but noted 53 specific locations with differences; lateral symmetry is a reasonable assumption for infants under 12 months.
Conclusions:
- This study establishes the first data-driven categorization of infant skull thickness distributions.
- Generated essential guidelines for age- and sex-based computational models in predicting infant head trauma.
- Findings contribute to improved injury prediction accuracy and prevention of child abuse through enhanced biomechanical analysis.
Abstract:
Child abuse remains a global issue, with infants under 1 year of age facing the highest risk of fatality and recurrence if abuse is not detected. Computational modeling is a powerful tool for predicting injury from real-world trauma, offering a means to validate caretaker-reported histories and prevent further abuse. A key challenge and gap, however, lies in capturing the natural anatomical variability within a population to enhance injury prediction accuracy. This study addresses this gap by quantifying skull thickness distributions in a robust sample (n = 266) and establishing data-driven anatomical standards based on similarities in thickness patterns. The study examined age and head circumference as predictors of skull thickness growth. For infants younger than 2.5 months, head circumference was a more reliable predictor than age. Infants under 12 months old were categorized into four age groups-0-1.5, 1.5-5.9, 5.9-10.2, and 10.2-12 months-using natural thickness distribution breaks and a variance optimization routine. No significant sex differences were found in average skull thickness within each cranial bone (left and right parietal, frontal, and occipital), but there were 53 locations with significant sex differences at various stages of development. Symmetry tests suggested that lateral symmetry may be an appropriate assumption for infants under 12 months. Representative thickness distributions for each age group were selected based on similarity scores. This study is the first to apply data-driven methods to categorize infant skull thickness distributions, generating essential guidelines for age- and sex-based models in predicting injury from head trauma in infants.
