Analyzing complex skeletal trauma can present a challenge for forensic practitioners to reliably determine the causes and circumstances of traumatic injury. The forensic value of skeletal fracture pattern analysis can be diminished due to obscuration and similarity between injuries associated with various impact mechanisms (e.g., vertical vs horizontal blunt force) and can provide issues when questions arise surrounding circumstances of traumatic injuries. Using the Python coding language, code was written that segments traumatized regions of interest from post-mortem computed tomography (PMCT) scans, allowing the user to calculate percentage of fragmentation in the context of extreme trauma events. Using cases of known trauma which resulted in fragmentation of the femur, the objective was to assess if there were statistical differences in the fragmentation resulting from horizontal pedestrian motor vehicle impacts (PMVIs; n = 44) compared to vertical high impact falls (>3 m; n = 41). Results indicated that percentage differences between the PMVI group and high impact falls group were statistically significant. Although it was possible to develop a standardized method that records fragmentation, and results were significant in distinguishing between the two groups, the outcomes of the data follow an exponential distribution which has implications for how skeletal trauma is modeled.
Keywords: exponential distribution; forensic anthropology; fragmentation; impact mechanism; modeling; post-mortem CT; skeletal trauma.
© 2021 American Academy of Forensic Sciences.