Personalization of closed-chain shoulder models yields high kinematic accuracy for multiple motions.

INTRODUCTION

The shoulder joint complex is prone to musculoskeletal issues, such as rotator cuff-related pain, which affect two-thirds of adults and often result in suboptimal treatment outcomes. Current musculoskeletal models used to understand shoulder biomechanics are limited by challenges in personalization, inaccuracies in predicting joint and muscle loads, and an inability to simulate anatomically accurate motions. To address these deficiencies, we developed a novel, personalized modeling framework capable of calibrating subject-specific joint centers and functional axes for the shoulder complex.

METHODS

We developed a novel personalized modeling framework utilizing the Joint Model Personalization (JMP) Tool from the Neuromusculoskeletal Modeling Pipeline, incorporating in vivo biplane fluoroscopy data of the glenohumeral and scapulothoracic joints. Initially, open-chain scapula-only models with 3, 4, and 5 degrees of freedom (DOFs) were created and optimized using synthetic marker data derived from subject-specific geometry. Subsequently, closed-chain shoulder models including scapula, clavicle, and humerus were constructed and optimized through a two-stage personalization approach. Model accuracy and generalizability were assessed using marker distance errors and leave-one-out cross-validation across multiple shoulder motions.

RESULTS

Increasing the number of scapula DOFs in open-chain models improved kinematic accuracy, with the 5 DOF scapula model yielding the lowest marker distance errors (average: 0.8 mm; maximum: 5.2 mm). The closed-chain shoulder model demonstrated high accuracy (average: 0.9 mm; maximum: 5.7 mm) and consistency across subject in cross-validation tests (average marker distance errors = 1.0-1.4 mm). Models personalized with synthetic noise representative of skin-based marker data resulted in slightly increased, yet acceptable marker errors (average: 3.4 mm).

CONCLUSION

Our personalized, closed-chain shoulder modeling framework significantly improves the accuracy and anatomical fidelity of shoulder kinematic simulations compared to existing approaches. This framework minimizes errors in joint kinematics and provides a foundation for future models incorporating personalized musculature and advanced simulations.