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Prediction of Skull Fracture Risk for Children 0-9 Months Old Through Validated Parametric Finite Element Model and Cadaver Test Reconstruction

NCJ Number
310116
Journal
International Journal of Legal Medicine Volume: 129 Dated: April 2015 Pages: 1055-1066
Author(s)
Zhigang Li; Weiguo Liu; Jinhuan Zhang; Jingwen Hu
Date Published
April 2015
Length
12 pages
Annotation

This document reports on a research project that reconstructed 50 pediatric cadaver tests using a parametric pediatric head finite element model; and it discusses the project findings, which demonstrated the feasibility of using age- and size/shape-appropriate head FE models to predict head injuries in children.

Abstract

Skull fracture is one of the most common pediatric traumas. However, injury assessment tools for predicting pediatric skull fracture risk is not well established mainly due to the lack of cadaver tests. Weber conducted 50 pediatric cadaver drop tests for forensic research on child abuse in the mid-1980s. To the authors’ knowledge, these studies contained the largest sample size among pediatric cadaver tests in the literature. However, the lack of injury measurements limited their direct application in investigating pediatric skull fracture risks. In this study, 50 pediatric cadaver tests from Weber’s studies were reconstructed using a parametric pediatric head finite element (FE) model which were morphed into subjects with ages, head sizes/shapes, and skull thickness values that reported in the tests. The skull fracture risk curves for infants from zero to nine months old were developed based on the model-predicted head injury measures through logistic regression analysis. It was found that the model-predicted stress responses in the skull (maximal von Mises stress, maximal shear stress, and maximal first principal stress) were better predictors than global kinematic-based injury measures (peak head acceleration and head injury criterion (HIC)) in predicting pediatric skull fracture. This study demonstrated the feasibility of using age- and size/shape-appropriate head FE models to predict pediatric head injuries. Such models can account for the morphological variations among the subjects, which cannot be considered by a single FE human model. (Published Abstract Provided)