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Modeling of inflicted head injury by shaking trauma in children: what can we learn? : Update to parts I&II: A systematic review of animal, mathematical and physical models.

Kim HutchinsonJan Peter van ZandwijkMarloes E M VesterAjay SethRob A C BiloRick Robert van RijnArjo J Loeve
Published in: Forensic science, medicine, and pathology (2024)
Inflicted shaking trauma can cause injury in infants, but exact injury mechanisms remain unclear. Controversy exists, particularly in courts, whether additional causes such as impact are required to produce injuries found in cases of (suspected) shaking. Publication rates of studies on animal and biomechanical models of inflicted head injury by shaking trauma (IHI-ST) in infants continue rising. Dissention on the topic, combined with its legal relevance, makes maintaining an up-to-date, clear and accessible overview of the current knowledge-base on IHI-ST essential. The current work reviews recent (2017-2023) studies using models of IHI-ST, serving as an update to two previously published reviews. A systematic review was conducted in Scopus and PubMed for articles using animal, physical and mathematical models for IHI-ST. Using the PRISMA methodology, two researchers independently screened the publications. Two, five, and ten publications were included on animal, physical, and mathematical models of IHI-ST, respectively. Both animal model studies used rodents. It is unknown to what degree these can accurately represent IHI-ST. Physical models were used mostly to investigate gross head-kinematics during shaking. Most mathematical models were used to study local effects on the eye and the head's internal structures. All injury thresholds and material properties used were based on scaled adult or animal data. Shaking motions used as inputs for animal, physical and mathematical models were mostly greatly simplified. Future research should focus on using more accurate shaking inputs for models, and on developing or and validating accurate injury thresholds applicable for shaking.
Keyphrases
  • physical activity
  • mental health
  • high resolution
  • randomized controlled trial
  • machine learning
  • deep learning
  • big data