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的前十字韧带 (ACL) 撕裂，造成超过 20 亿美元的治疗费用。 ACL 损伤的机制尚不清楚，因为之前的研究无法将体育活动期间的膝关节运动学与 ACL 的应变联系起来。此外，没有可用的工具来识别 ACL 损伤高风险的个体。 除非了解高风险活动期间膝关节运动学与 ACL 应变之间的关系，否则不可能对运动员进行 ACL 损伤风险筛查并设计损伤预防方法。 为了了解高风险活动期间膝关节运动学与 ACL 应变之间的关系，提出了一种独特的体内/体外组合方法。这种实验方法已经开发了四年。在这种方法中，将对执行高风险活动的个人进行动作捕捉。运动捕捉数据将被输入到下肢计算机模型中，以计算高风险活动期间特定肌肉群在膝盖上施加的力。然后，使用为此目的开发的膝关节损伤模拟器系统将这些肌肉力施加到装有仪器的尸体膝盖上。 将对 10 名男性和 10 名女性运动员进行的高风险活动的 20 具尸体膝盖进行 ACL 应变测量。然后将开发一个将外部可测量膝关节运动学参数与 ACL 应变相关联的经验模型。 开发的经验模型可用于识别 ACL 损伤高风险的个体。使用该模型，可以预测有抱负的运动员在进行高风险活动时的 ACL 应变。 然后，如果该运动员有 ACL 损伤的高风险，可以设计神经肌肉训练来在活动期间减轻 ACL 的负荷。这种方法将为 ACL 损伤预防提供循证培训，并开发针对高危运动员的护膝，从而减少医疗成本和人类痛苦。

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.