Department of Mechanical Engineering, University of Canterbury, Christchurch, 8041, New Zealand.
Faculty of Health, University of Canterbury, Christchurch, 8041, New Zealand.
Ann Biomed Eng. 2024 Aug;52(8):2234-2246. doi: 10.1007/s10439-024-03525-w. Epub 2024 May 13.
In contact sports such as rugby, players are at risk of sustaining traumatic brain injuries (TBI) due to high-intensity head impacts that generate high linear and rotational accelerations of the head. Previous studies have established a clear link between high-intensity head impacts and brain strains that result in concussions. This study presents a novel approach to investigating the effect of a range of laboratory controlled drop test parameters on regional peak and mean maximum principal strain (MPS) predictions within the brain using a trained convolutional neural network (CNN). The CNN is publicly available at https://github.com/Jilab-biomechanics/CNN-brain-strains . The results of this study corroborate previous findings that impacts to the side of the head result in significantly higher regional MPS than forehead impacts. Forehead impacts tend to result in the lowest region-averaged MPS values for impacts where the surface angle was at 0° and 45°, while side impacts tend to result in higher regional peak and mean MPS. The absence of a neck in drop tests resulted in lower regional peak and mean MPS values. The results indicated that the relationship between drop test parameters and resulting regional peak and mean MPS predictions is complex. The study's findings offer valuable insights into how deep learning models can be used to provide more detailed insights into how drop test conditions impact regional MPS. The novel approach used in this paper to predict brain strains can be applied in the development of better methods to reduce the brain strain resulting from head accelerations such as protective sports headgear.
在橄榄球等接触性运动中，由于高强度的头部冲击会产生头部的高线性和旋转加速度，运动员有遭受创伤性脑损伤（TBI）的风险。先前的研究已经确定了高强度头部冲击与导致脑震荡的脑应变之间的明确联系。本研究提出了一种新方法，使用经过训练的卷积神经网络（CNN）来研究一系列实验室控制的跌落测试参数对大脑内局部峰值和平均最大主应变（MPS）预测的影响。CNN 可在 https://github.com/Jilab-biomechanics/CNN-brain-strains 上公开获取。本研究的结果证实了先前的发现，即头部侧面的冲击会导致明显更高的局部 MPS，而额头冲击则会导致较低的局部 MPS。对于表面角度为 0°和 45°的冲击，额头冲击往往会导致最低的区域平均 MPS 值，而侧面冲击则会导致更高的局部峰值和平均 MPS。跌落测试中颈部的缺失导致了较低的局部峰值和平均 MPS 值。结果表明，跌落测试参数与产生的局部峰值和平均 MPS 预测之间的关系非常复杂。本研究的发现为深度学习模型如何用于更深入地了解跌落测试条件如何影响局部 MPS 提供了有价值的见解。本文中用于预测脑应变的新方法可应用于开发更好的方法，以减少头部加速度（如防护运动头盔）导致的脑应变。
