Anthropometric Scaling of Anatomical Datasets for Subject-Specific Musculoskeletal Modelling of the Shoulder

Christian Klemt,Miny Walker,Peter Reilly,Ziyun Ding,Mary E Finnegan,Anthony M J Bull,Rebecca A Quest,Lance Rane,Daniel Nolte

doi:10.1007/s10439-019-02207-2

Christian Klemt, Miny Walker + Show 7 more

Open Access

https://doi.org/10.1007/s10439-019-02207-2

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Abstract

Linear scaling of generic shoulder models leads to substantial errors in model predictions. Customisation of shoulder modelling through magnetic resonance imaging (MRI) improves modelling outcomes, but model development is time and technology intensive. This study aims to validate 10 MRI-based shoulder models, identify the best combinations of anthropometric parameters for model scaling, and quantify the improvement in model predictions of glenohumeral loading through anthropometric scaling from this anatomical atlas. The shoulder anatomy was modelled using a validated musculoskeletal model (UKNSM). Ten subject-specific models were developed through manual digitisation of model parameters from high-resolution MRI. Kinematic data of 16 functional daily activities were collected using a 10-camera optical motion capture system. Subject-specific model predictions were validated with measured muscle activations. The MRI-based shoulder models show good agreement with measured muscle activations. A tenfold cross-validation using the validated personalised shoulder models demonstrates that linear scaling of anthropometric datasets with the most similar ratio of body height to shoulder width and from the same gender (p < 0.04) yields best modelling outcomes in glenohumeral loading. The improvement in model reliability is significant (p < 0.02) when compared to the linearly scaled-generic UKNSM. This study may facilitate the clinical application of musculoskeletal shoulder modelling to aid surgical decision-making.

Highlights

Validated computational models of the musculoskeletal (MSK) system can be used to understand normal and pathological human movement by predicting articular and tissue loading, parameters that cannot currently be measured directly
As the number of openly available anatomical datasets with quantification of all upper limb muscle attachment sites and muscle volumes for shoulder modelling in the literature is small,[42] this study aims to develop and validate 10 magnetic resonance imaging (MRI)-based shoulder models against measured muscle activations and muscle moment arms, demonstrate the dependency of modelling results on anatomical geometry, identify the best combinations of anthropometric parameters that yield the smallest error in model estimations of glenohumeral joint contact force and muscle forces through scaling of personalised musculoskeletal shoulder models, and quantify the improvement in model reliability through anthropometric scaling of anatomical datasets when compared to a single, scaled-generic model
Customisation of musculoskeletal modelling through medical imaging has demonstrated significant improvements in model reliability when compared to linearly scaled-generic models,[19,38,39] but the model development is time, labour and technology intensive

Summary

Introduction

Validated computational models of the musculoskeletal (MSK) system can be used to understand normal and pathological human movement by predicting articular and tissue loading, parameters that cannot currently be measured directly. The precise knowledge of musculoskeletal loading is essential for clinical applications in order to improve surgical and rehabilitative treatment planning, assistive device design and analysis of joint arthroplasty design. The use of musculoskeletal models in clinical practice has been hampered by the dependency of modelling results on model input, in particular the anatomical geometry.[4,6] Linearly scaled-generic models, derived from the dissection of cadaveric specimens, are widely used to represent a subject’s anatomical geometry.[5,8,14,17,26,32] These models accommodate geometric variation across subjects through linear scaling, based on three-dimensional positions of anatomical landmarks.[25]. As linearly scaled-generic models do not account for individual variations in anthropometry such as muscle attachment sites and muscle volumes, they lead to errors in muscle path estimations that will result in substantial inaccuracies in calculated muscle and joint forces.[18,38]

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