A Combined in-Vivo/in-Vitro study of Anterior Cruciate Ligament Biomechanics

北美每年约有10万名年轻运动员膝盖内前十字韧带(ACL)撕裂，导致超过20亿美元的治疗费用。由于以前的研究不能将运动中的膝关节运动学与前交叉韧带的劳损联系起来，所以前交叉韧带损伤的机制还不是很清楚。此外，还没有可用的工具来识别前交叉韧带损伤的高危个体。除非了解高危活动中膝关节运动学和前交叉韧带劳损之间的关系，否则不可能对运动员进行前交叉韧带损伤风险筛查和设计损伤预防方法。 为了了解高危活动中膝关节运动学与前交叉韧带拉伤的关系，提出了一种独特的体内/体外联合方法。这一实验方法已经开发了四年。在这种方法中，将执行执行高风险活动的个人的动作捕获。运动捕捉数据将被输入到腿部的计算机模型中，以计算在高风险活动期间特定肌肉群在膝盖上施加的力。然后，这些肌肉力量将使用为此目的开发的膝关节损伤模拟系统应用于仪器化的身体膝盖。在10名男运动员和10名女运动员进行的高风险活动中，将在20具身体膝盖上测量前交叉韧带拉伤。然后将建立一个将外部可测量的膝关节运动学参数与前交叉韧带应变相关联的经验模型。 所建立的经验模型可用于识别前交叉韧带损伤的高危个体。使用该模型，可以预测有抱负的运动员在进行高风险活动期间的前交叉韧带应变。然后，如果运动员有前交叉韧带损伤的高风险，可以设计神经肌肉训练来在活动期间卸载前交叉韧带。这一方法将为预防前交叉韧带损伤提供循证培训，并针对高危运动员开发膝关节支架，从而减少医疗费用和人类痛苦。

About 100,000 young athletes in North America tear their anterior cruciate ligament (ACL) within the knee every year, resulting in more than 2 billion dollars of treatment costs. The mechanism of ACL injury is not well understood because previous studies have been unable to relate knee kinematics during sports activities to the strain in the ACL. Further, no tools are available to identify individuals who are at high risk of ACL injury. Unless the relationship between knee kinematics and ACL strain during high-risk activities is understood, screening of athletes for ACL injury risk and design of injury prevention methodologies is not possible. To understand the relationship between knee kinematics and ACL strain during high-risk activities, a unique combined in-vivo/in-vitro method is proposed. This experimental approach has been four years in development. In this approach, motion capture of individuals performing high-risk activities will be performed. The motion capture data will be input into a computer model of lower extremity to calculate the forces that specific muscle groups are applying across the knee during high-risk activities. These muscle forces will then be applied on instrumented cadaver knees using a knee injury simulator system that was developed for this purpose. The ACL strain will be measured on 20 cadaver knees for high risk activities performed by 10 male and 10 female athletes. An empirical model that correlates the external measurable knee kinematic parameters to the ACL strain will then be developed. The empirical model developed can be used to identify the individuals who have high risk of ACL injury. Using the model, it will be possible to predict the ACL strain during high-risk activity performed by an aspiring athlete. Then, if that athlete has high risk of ACL injury, neuromuscular training can be devised to unload the ACL during the activity. This approach will provide evidence-based training for ACL injury prevention and to develop knee braces targeted towards high-risk athletes resulting in reduced health care costs and human suffering.