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[Evaluation of brain injury caused by stick type blunt instruments based on convolutional neural network and finite element method].

Haiyan LiHaifang LiGuanglong HeWengang LiuShihai CuiLijuan HeWenle LuJian-Yu Pan

2022PubMedEngineering被引 1开放获取

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摘要

) 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.

引用本文(GB/T 7714)

Haiyan Li, Haifang Li, Guanglong He, 等. [Evaluation of brain injury caused by stick type blunt instruments based on convolutional neural network and finite element method].[J]. PubMed, 2022.

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DOI:https://doi.org/10.7507/1001-5515.202106087

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