Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01605, USA.
Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, 24061, USA.
Ann Biomed Eng. 2022 Nov;50(11):1498-1509. doi: 10.1007/s10439-022-03005-z. Epub 2022 Jul 11.
Brain strain is increasingly being used in helmet design and safety performance evaluation as it is generally considered as the primary mechanism of concussion. In this study, we investigate whether different helmet designs can meaningfully alter brain strains using two commonly used metrics, peak maximum principal strain (MPS) of the whole brain and cumulative strain damage measure (CSDM). A convolutional neural network (CNN) that instantly produces detailed brain strains is first tested for accuracy for helmeted head impacts. Based on N = 144 impacts in 12 impact conditions from three random and representative helmet models, we conclude that the CNN is sufficiently accurate for helmet testing applications, for elementwise MPS (success rate of 98.6%), whole-brain peak MPS and CSDM (coefficient of determination of 0.977 and 0.980, with root mean squared error of 0.015 and 0.029, respectively). We then apply the technique to 23 football helmet models (N = 1104 impacts) to reproduce elementwise MPS. Assuming a concussion would occur when peak MPS or CSDM exceeds a threshold, we sweep their thresholds across the value ranges to evaluate the number of predicted hypothetical concussions that different helmets sustain across the impact conditions. Relative to the 12 impact conditions tested, we find that the "best" and "worst" helmets differ by an average of 22.5% in terms of predicted concussions, ranging from 0 to 42% (the latter achieved at the threshold value of 0.28 for peak MPS and 0.4 for CSDM, respectively). Such a large variation among helmets in strain-based concussion predictions demonstrate that helmet designs can still be optimized in a clinically meaningful way. The robustness and accuracy of the CNN tool also suggest its potential for routine use for helmet design and safety performance evaluation in the future. The CNN is freely available online at https://github.com/Jilab-biomechanics/CNN-brain-strains .
脑应变越来越多地被用于头盔设计和安全性能评估，因为它通常被认为是脑震荡的主要机制。在这项研究中，我们研究了不同的头盔设计是否可以使用两种常用的指标，即整个大脑的峰值最大主应变（MPS）和累积应变损伤测量（CSDM），显著改变大脑应变。首先，我们测试了一个卷积神经网络（CNN），该网络可以立即生成详细的大脑应变，以验证其在头盔冲击中的准确性。基于来自三个随机和有代表性的头盔模型的 12 种冲击条件的 144 次冲击，我们得出结论，该 CNN 对于头盔测试应用来说足够准确，对于元素级 MPS（成功率为 98.6%）、整个大脑峰值 MPS 和 CSDM（决定系数分别为 0.977 和 0.980，均方根误差分别为 0.015 和 0.029）。然后，我们将该技术应用于 23 个橄榄球头盔模型（N=1104 次冲击），以重现元素级 MPS。假设当峰值 MPS 或 CSDM 超过阈值时会发生脑震荡，我们在整个阈值范围内扫描它们的阈值，以评估不同头盔在冲击条件下承受的预测假设性脑震荡的数量。与测试的 12 种冲击条件相比，我们发现“最佳”和“最差”头盔在预测脑震荡方面的差异平均为 22.5%，范围从 0 到 42%（后者分别在峰值 MPS 阈值为 0.28 和 CSDM 阈值为 0.4 时达到）。头盔在基于应变的脑震荡预测中的这种大的差异表明，头盔设计仍然可以以有临床意义的方式进行优化。CNN 工具的稳健性和准确性也表明了它在未来头盔设计和安全性能评估中的常规使用潜力。该 CNN 可在网上免费获取，网址为 https://github.com/Jilab-biomechanics/CNN-brain-strains。
