### Abstract

This article introduces a new approach for the construction of a risk model for the prediction of Traumatic Brain Injury (TBI) as a result of a car crash. The probability of TBI is assessed through the fusion of an experiment-based logistic regression risk model and a finite element (FE) simulation-based risk model. The proposed approach uses a multilevel framework which includes FE simulations of vehicle crashes with dummy and FE simulations of the human brain. The loading conditions derived from the crash simulations are transferred to the brain model thus allowing the calculation of injury metrics such as the Cumulative Strain Damage Measure (CSDM). The framework is used to propagate uncertainties and obtain probabilities of TBI based on the CSDM injury metric. The risk model from FE simulations is constructed from a support vector machine classifier, adaptive sampling, and Monte-Carlo simulations. An approach to compute the total probability of TBI, which combines the FE-based risk assessment as well as the risk prediction from the experiment-based logistic regression model is proposed. In contrast to previous published work, the proposed methodology 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 TBI for a given assumed crash impact velocity.

Original language | English (US) |
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Pages (from-to) | 605-619 |

Number of pages | 15 |

Journal | Computer Methods in Biomechanics and Biomedical Engineering |

Volume | 22 |

Issue number | 6 |

DOIs | |

Publication status | Published - Apr 26 2019 |

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### Keywords

- Crash simulations
- Data fusion
- Finite element analysis
- Risk model
- Traumatic Brain Injury

### ASJC Scopus subject areas

- Bioengineering
- Biomedical Engineering
- Human-Computer Interaction
- Computer Science Applications

### Cite this

*Computer Methods in Biomechanics and Biomedical Engineering*,

*22*(6), 605-619. https://doi.org/10.1080/10255842.2019.1574343