Citation

Li H, Li H, He G, Liu W, Cui S, He L, Lu W, Pan J, Zhou Y. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2022; 39(2): 276-284.

Vernacular Title

基于卷积神经网络和有限元法的棍棒类钝器致脑损伤评价方法研究

Copyright

(Copyright © 2022, Sichuan Sheng sheng wu yi xue gong cheng xue hui)

DOI

10.7507/1001-5515.202106087

PMID

35523548

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.

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有限元法作为研究钝器致颅脑损伤机制的新方法，存在耗时长、专业性强等技术壁垒，影响其推广应用。基于此，本研究提出了一种基于卷积神经网络和有限元方法的棍棒类钝器致颅脑损伤的快速量化评价方法。该方法以有限元仿真中提取的棍棒类钝器速度曲线以及脑组织（大脑、胼胝体、小脑、脑干）最大主应变分别作为卷积神经网络的输入与输出参数，并通过十折交叉验证法训练并优化卷积神经网络，最终确定的卷积神经网络模型对大脑最大主应变预测结果的平均绝对误差（MAE）、均方误差（MSE）、拟合优度（R2）分别为0.084、0.014、0.92；对胼胝体最大主应变预测结果的MAE、MSE、R2分别为0.062、0.007、0.90；对小脑及脑干最大主应变预测结果的MAE、MSE、R2分别为0.075、0.011、0.94。预测结果显示，本研究开发的深度卷积神经网络，能够快速而准确地评估由棍棒类钝器打击引起的局部脑损伤，并对理解其造成的脑损伤与量化评价具有重要的应用价值。同时，该技术提高了计算效率，可为将当前基于加速度的脑损伤研究转变为关注局部脑组织损伤的研究提供依据。

引用本文： 李海岩, 李海防, 何光龙, 刘文港, 崔世海, 贺丽娟, 吕文乐, 潘建宇, 周亦武. 基于卷积神经网络和有限元法的棍棒类钝器致脑损伤评价方法研究. 生物医学工程学杂志, 2022, 39(2): 276-284. doi: 10.7507/1001-5515.202106087 复制

Language: zh

Keywords

Finite element analysis; Convolutional neural network; Local brain injury; Stick blunt