Predictions of Anterior Cruciate Ligament Dynamics From Subject-Specific Musculoskeletal Models and Dynamic Biplane...

Area of Science:

• Biomechanics
• Musculoskeletal modeling
• Orthopedics

Background:

• Estimating in vivo knee ligament forces is crucial for rehabilitation and clinical interventions.
• Direct measurement of ligament forces during functional activities is challenging.
• Musculoskeletal models are primary tools for estimating in vivo ligament loading, but previous anterior cruciate ligament (ACL) force estimates vary widely.

Purpose of the Study:

• To compare subject-specific (SS) and scaled-generic (SG) musculoskeletal models for predicting ACL forces during gait.
• To assess the impact of individualized knee joint anatomy on ACL force predictions.
• To evaluate the feasibility of generating SS musculoskeletal models for in vivo tissue loading prediction.

Main Methods:

• Developed ten subject-specific (SS) lower limb musculoskeletal models with individualized geometry, muscle architecture, and knee joint kinematics from dynamic biplane radiography (DBR).
• Estimated ACL forces (anteromedial-aACL and posterolateral-pACL bundles) during treadmill walking gait cycle.
• Compared SS ACL force predictions with those from scaled-generic (SG) musculoskeletal models.

Main Results:

• SS models predicted a double force peak during stance (0.39-0.43 xBW per bundle), unlike SG models which showed a single peak for aACL.
• While continuous forces showed no significant difference, root mean-squared differences between SS and SG predictions ranged from 0.08 to 0.27 xBW.
• ACL force predictions were highly sensitive to ligament resting length, with ±10% variation causing up to 84% force differences.

Conclusions:

• Subject-specific musculoskeletal models are essential for accurate in vivo ACL force prediction due to sensitivity to individual anatomy (joint geometry, ligament resting length).
• Scaled-generic models may not reliably reflect subject-specific ACL forces.
• Generating SS musculoskeletal models is feasible for predicting in vivo tissue loading during functional activities.