Assessment and prediction of acute subdural hematomas in vehicle collision accidents. Issue 5 (5th October 2022)

• Record Type: Journal Article
• Authors: Liu, Qiming; Wu, Xingfu; Tong, Nichen
• Is Part Of: International journal of crashworthiness. Volume 27:Issue 5(2022)
• Page Start: 1444
• Page End: 1459
• Publication Date: 2022-10-05
• Subjects: Acute subdural hematoma -- injury criteria -- biomechanical metrics -- prediction model -- vehicle collision accident; Automobiles -- Crashworthiness -- Periodicals; Airplanes -- Crashworthiness -- Periodicals; 629.2042
• DOI: 10.1080/13588265.2021.1959169
• Languages: English
• ISSNs: 1358-8265

Abstract

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.