Evaluation of brain injury caused by stick type blunt instruments based on convolutional neural network and finite element method.
10.7507/1001-5515.202106087
- Author:
Haiyan LI
1
;
Haifang LI
1
;
Guanglong HE
2
;
Wengang LIU
1
;
Shihai CUI
1
;
Lijuan HE
1
;
Wenle LU
1
;
Jianyu PAN
1
;
Yiwu ZHOU
3
Author Information
1. International Research Association on Emerging Automotive Safety Technology, Tianjin 300222, P. R. China.
2. Institute of Forensic Science, Ministry of Public Security, Beijing 100038, P. R. China.
3. Department of Forensic Medicine, Tongji Medical College of Huazhong University of Science and Technology, Wuhan 430010, P. R. China.
- Publication Type:Journal Article
- Keywords:
Convolutional neural network;
Finite element analysis;
Local brain injury;
Stick blunt
- MeSH:
Brain;
Brain Injuries;
Computer Simulation;
Finite Element Analysis;
Humans;
Neural Networks, Computer
- From:
Journal of Biomedical Engineering
2022;39(2):276-284
- CountryChina
- Language:Chinese
-
Abstract:
The finite element method is a new method to study the mechanism of brain injury caused by blunt instruments. But it is not easy to be applied because of its technology barrier of time-consuming and strong professionalism. In this study, a rapid and quantitative evaluation method was investigated to analyze the craniocerebral injury induced by blunt sticks based on convolutional neural network and finite element method. The velocity curve of stick struck and the maximum principal strain of brain tissue (cerebrum, corpus callosum, cerebellum and brainstem) from the finite element simulation were used as the input and output parameters of the convolutional neural network The convolutional neural network was trained and optimized by using the 10-fold cross-validation method. The Mean Absolute Error (MAE), Mean Square Error (MSE), and Goodness of Fit ( R 2) of the finally selected convolutional neural network model for the prediction of the maximum principal strain of the cerebrum were 0.084, 0.014, and 0.92, respectively. The predicted results of the maximum principal strain of the corpus callosum were 0.062, 0.007, 0.90, respectively. The predicted results of the maximum principal strain of the cerebellum and brainstem were 0.075, 0.011, and 0.94, respectively. These results show that the research and development of the deep convolutional neural network can quickly and accurately assess the local brain injury caused by the sticks blow, and have important application value for understanding the quantitative evaluation and the brain injury caused by the sticks struck. At the same time, this technology improves the computational efficiency and can provide a basis reference for transforming the current acceleration-based brain injury research into a focus on local brain injury research.
