A computational musculoskeletal model for ACL injury risk analysis: Development and validation

Area of Science:

• Biomechanics
• Computational modeling
• Orthopedics

Background:

• Anterior cruciate ligament (ACL) injuries are common, but their underlying biomechanical mechanisms are not fully understood.
• Existing computational musculoskeletal models often lack integrated multi-level features for detailed joint analysis.

Purpose of the Study:

• To develop and validate a novel, integrated computational musculoskeletal model for quantifying joint-level biomechanics during dynamic movements.
• To investigate the relationships between kinematic variables, muscle forces, and ACL strain/force during high-risk movements.

Main Methods:

• Integrated a discrete-element knee model with a full-body musculoskeletal model.
• Validated ligament and muscle geometries and knee ligament mechanics.
• Applied the model to simulate high-risk movements in healthy participants and performed regression analysis.

Main Results:

• The model accurately preserved ligament and muscle geometries and demonstrated robust knee joint stability.
• Increased ACL strain was associated with knee abduction and anterior tibial translation during landing.
• Quadriceps, gastrocnemius, and adductor forces increased ACL loading, while hamstring forces showed a task-dependent effect.

Conclusions:

• The developed computational model is a powerful tool for understanding ACL injury mechanisms.
• It can identify biomechanical risk factors and inform the development of evidence-based prevention strategies for ACL injuries.