title: Classification of Porcine Cranial Fracture Patterns Using a Fracture Printing Interface,<sup/>.
abstract: Distinguishing between accidental and abusive head trauma in children can be difficult, as 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. In the current study, an automated pattern recognition method, or a fracture printing interface (FPI), was developed to classify cranial fracture patterns that were associated with different impact scenarios documented in previous experiments. 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% 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.
authors.affiliation: Orthopaedic Biomechanics Laboratories, Michigan State University, East Lansing, MI, 48824.|Department of Radiology, Michigan State University, East Lansing, MI, 48824.|Department of Mechanical Engineering, Michigan State University, East Lansing, MI, 48824.
authors.forename: Feng
authors.identifier: 0000-0002-0749-5582
authors.initials: F
authors.lastname: Wei
authors.affiliation: Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824.
authors.forename: Serhat Selçuk
authors.initials: SS
authors.lastname: Bucak
authors.affiliation: Department of Anthropology, Michigan State University, East Lansing, MI, 48824.
authors.forename: Jennifer M
authors.initials: JM
authors.lastname: Vollner
authors.affiliation: Department of Anthropology, Michigan State University, East Lansing, MI, 48824.
authors.forename: Todd W
authors.initials: TW
authors.lastname: Fenton
authors.affiliation: Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824.
authors.forename: Anil K
authors.initials: AK
authors.lastname: Jain
authors.affiliation: Orthopaedic Biomechanics Laboratories, Michigan State University, East Lansing, MI, 48824.|Department of Radiology, Michigan State University, East Lansing, MI, 48824.|Department of Mechanical Engineering, Michigan State University, East Lansing, MI, 48824.
authors.forename: Roger C
authors.initials: RC
authors.lastname: Haut
mesh_terms: D000059:Accidents; D000818:Animals; D002648:Child; D002649:Child Abuse / Q000175:diagnosis; D003937:Diagnosis, Differential; D018732:Forensic Anthropology / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D023421:Models, Animal; D012887:Skull Fractures / Q000145:classification*; D012984:Software; D013552:Swine
keywords: abusive head trauma; animal model; forensic anthropology; forensic science; infant skull fracture; machine learning; pattern recognition; pediatric injury