The anterior cruciate ligament (ACL) is one of the most frequently injured ligaments of the knee, with a prevalence estimated to be 1 in 3000 in the U.S. population. Current approaches to biomechanical finite element (FE) modeling of the knee are at a crossroads. While the ideal scenario for clinically applicable FE modeling would be a subject-specific approach with detailed, image-based anatomic reconstruction of the joint, the computational intensity of such an approach would almost certainly preclude its clinical applicability. The assumption that an accurate assessment of an individual’s ACL injury risk profile can be attained through generalized FE models also has yet to be tested. The current study aims to test a novel framework in which the developed, validated generic FE model will be customized to each specimen based on quantified ACL structural properties (mechanical and anatomical). We hypothesized that personalized FE models using the proposed framework will result in more accurate predictions of ACL strain (as an established measure of injury risk) compared to the generic FE model. Thus, these models may serve as individual-based injury risk assessment tools.

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