Risk prediction of traumatic brain injury from car accidents

Seyed Saeed Ahmadisoleymani, Samy Missoum

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The purpose of this study is to build a risk model to predict the probability of Traumatic Brain Injury (TBI). The focus is on the occurrence of one of TBI outcomes, Diffuse Axonal Injury (DAI), due to car crashes. This goal is achieved by developing a multilevel framework, which includes vehicle crash Finite Element (FE) simulations with a dummy along with FE simulations of the brain using loading conditions derived from the crash simulations. The framework is used to propagate uncertainties and obtain probabilities of DAI based on certain injury criteria such as Cumulative Strain Damage Measure (CSDM). The risk model is constructed from a support vector machine classifier, adaptive sampling, and Monte-Carlo simulations. In contrast to previous risk models, it includes the uncertainty of explicit parameters such as impact conditions (e.g., velocity, impact angle), and material properties of the brain model. This risk model can provide, for instance, the probability of DAI for a given assumed velocity.

Original languageEnglish (US)
Title of host publicationBiomedical and Biotechnology Engineering
PublisherAmerican Society of Mechanical Engineers (ASME)
Volume3
ISBN (Electronic)9780791858363
DOIs
StatePublished - Jan 1 2017
EventASME 2017 International Mechanical Engineering Congress and Exposition, IMECE 2017 - Tampa, United States
Duration: Nov 3 2017Nov 9 2017

Other

OtherASME 2017 International Mechanical Engineering Congress and Exposition, IMECE 2017
CountryUnited States
CityTampa
Period11/3/1711/9/17

ASJC Scopus subject areas

  • Mechanical Engineering

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  • Cite this

    Ahmadisoleymani, S. S., & Missoum, S. (2017). Risk prediction of traumatic brain injury from car accidents. In Biomedical and Biotechnology Engineering (Vol. 3). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/IMECE2017-70624