Personalization of Closed-Chain Shoulder Models Yields High Kinematic Accuracy for Multiple Motions

Abstract

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. Leveraging in vivo biplane fluoroscopy data and the recent Joint Model Personalization Tool from the Neuromusculoskeletal Modeling Pipeline, we optimized joint parameters and body scale factors for shoulder models with varying degrees of freedom (DOFs). We initially created and tested open-chain scapula-only models (3DOF, 4DOF, and 5DOF) and found that increasing DOFs improved accuracy, with the 5 DOF model yielding the lowest marker distance errors (average = 0.8 mm, maximum = 5.2 mm) as compared to biplane fluorscopy data of the scapula across eight movement trials. We subsequently created closed-chain shoulder models incorporating scapula, clavicle, and humerus bodies. We found closed-chain shoulder models with 5 DOFs for the scapula achieved the highest accuracy (average = 0.9 mm, maximum = 5.7 mm) and showed consistent performance across subjects (n=3) in leave-one-out cross-validation tests (average marker distance errors = 1.0-1.4 mm). This framework minimizes errors in joint kinematics and provides a foundation for future models incorporating personalized musculature and advanced simulations, enhancing its potential clinical utility for rehabilitation and surgical planning.