Open AccessEvaluation of brain injury criteria based on reliability analysis
Author(s) -
Máté Hazay,
Imre Bojtár
Publication year - 2021
Publication title -
acta of bioengineering and biomechanics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.361
H-Index - 25
eISSN - 2450-6303
pISSN - 1509-409X
DOI - 10.37190/abb-01755-2020-04
Subject(s) - weibull distribution , reliability (semiconductor) , bric , statistics , receiver operating characteristic , log normal distribution , risk assessment , mathematics , computer science , algorithm , medicine , physics , power (physics) , emerging markets , computer security , quantum mechanics , economics , macroeconomics
Purpose: Among the proposed brain injury metrics, Brain Injury Criteria (BrIC) is a promising tool for performing safety assessment of vehicles in the future. In this paper, the available risk curves of BrIC were re-evaluated with the use of reliability analysis and new risk curves were constructed for different injury types based on literature data of tissue-level tolerances. Moreover, the comparison of different injury metrics and their corresponding risk curves were performed. Methods: Tissue-level uncertainties of the effect and resistance were considered by random variables. The variability of the tissue-level predictors was quantified by the finite element reconstruction of 100 frontal crash tests which were performed in Simulated Injury Monitor environment. The applied tests were scaled to given BrIC magnitudes and the injury probabilities were calculated by Monte Carlo simulations. New risk curves were fitted to the observed results using Weibull and Lognormal distribution functions. Results: The available risk curves of diffuse axonal injury (DAI) could be slightly improved, and combined AIS 4+ risk curves were obtained by considering subdural hematoma and contusion as well. The performance of several injury metrics and their risk curves were evaluated based on the observed correlations with the tissue-level predictors. Conclusions: The cumulative strain damage measure and the BrIC provide the highest correlation (R 2 = 0.61) and the most reliable risk curve for the evaluation of DAI. Although the observed correlation is smaller for other injury types, the BrIC and the associated reliability analysis-based risk curves seem to provide the best available method for estimating the brain injury risk for frontal crash tests.