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Classification of Porcine Cranial Fracture Patterns Using a Fracture Printing Interface

NCJ Number
252437
Date Published
2017
Length
11 pages
Author(s)
Feng Wei; Serhat S. Bucak; Jennifer M. Vollner; Todd W. Fenton; Anil K. Jain; Roger C. Haut
Agencies
NIJ-Sponsored
Annotation
This study developed an automated pattern recognition method, or a fracture printing interface (FPI), to classify cranial fracture patterns that were associated with different impact scenarios documented in previous experiments.
Abstract
Distinguishing between accidental and abusive head trauma in children can be difficult, since there is a lack of baseline data for pediatric cranial fracture patterns. A porcine head model has recently been developed and utilized in a series of studies to investigate the effects of impact energy level, surface type, and constraint condition on cranial fracture patterns. The FPI accurately predicted the energy level when the impact surface type was rigid. Additionally, the FPI was exceedingly successful in determining fractures caused by skulls being dropped with a high level energy (97 percent accuracy). The FPI, currently developed on the porcine data, may in the future be transformed to the task of cranial fracture pattern classification for human infant skulls. (Publisher abstract modified)

Date Created: July 20, 2021