Assessment and prediction of acute subdural hematomas in vehicle collision accidents

Abstract

Acute subdural hematomas (ASDHs) caused by vehicle collisions are common brain injuries that suffers prone to disability and death. However, there is currently no commonly accepted evaluation index for ASDHs, such as the head injury criterion (HIC) for evaluating skull injuries and the brain injury criteria (BrIC, BRIC) for evaluating diffuse axonal injuries. Therefore, we established a set of ASDH indexes (ASDHIs) based on the genetic algorithm for different biomechanical metrics and developed an ASDH prediction model based on an artificial neural network learning (ANN-L). First, simulation models were built using 218 sets of crash test data and the simulated injury monitor model (SIMon). We calculated the correlation coefficients between the kinematic parameters, such as the translational and rotational acceleration, and the biomechanical metrics, such as the intracranial pressure and strain of the bridging vein. The effect of some outlier data is removed using a combination of K-Nearest Neighbor and quadratic regression. We screened some parameters with strong correlations and calculated their weight factors based on the genetic algorithm to establish a set of evaluation indexes for ASDHs. Comparing with 19 existed injury criteria, the results show that each established ASDHI has a higher correlation than these traditional criteria, and also indicate that the evaluation performance of each established ASDHI is advantageous. Finally, on the basis of these established ASDHIs, we built an accurate prediction model of ASDHs using ANN-L, which makes it possible to realize the prediction of ASDHs by only depending on kinematic responses.

Keywords:

• Acute subdural hematoma
• injury criteria
• biomechanical metrics
• prediction model
• vehicle collision accident

Acknowledgements

The authors would like to thank the National Highway Traffic Safety Administration (NHTSA) for providing the vehicle test data and SIMon model. The authors also appreciate the National Supercomputing Center in Changsha (NSCC) for offering reliable computing services.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported by the Natural Science Foundation of Hebei Province of China (Grant No. A2019202171), Independent Research Project of State Key Laboratory of Reliability and Intelligence Electrical Equipment, Hebei University of Technology, PR China (No. EERIPD2018002), the Open Fund of State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, PR China (Grant No. 31915004).