Anterior cruciate ligament (ACL) injury is a serious concern for athletes, such as soccer
players. In young, active athletes – the population most at risk of sustaining an ACL injury – re-injury or contralateral ACL injury can be as high as 20%. Identifying mechanical markers that may indicate potential risk of such re-injuries is thus a critical element of safe return to sport. Computational methods can address this need by providing means to study factors that cannot be studied directly in vivo or in vitro. Assessment of factors such as injury kinematics, ACL tissue and knee geometry, material properties, etc, performed using finite element methods may help to understand how non-contact ACL injury occurs or if there is predisposition for re-injury.

The goal of this study is to assess and identify potential mechanical markers which may indicate predisposition to ACL injuries and subsequently post-traumatic osteoarthritis.