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

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.

Accuracy and kinematics consistency of marker-based scaling approaches on a lower limb model: a comparative study with imagery data

“…Non-linear functions could also be used to enhance the scaling properties before an optimisation as in MB a:m à : , as it has been done in Lund et al (2015); Zhang et al (2016); Nolte et al (2019). Another enhancement may be to use a database of models extracted from MRIs and to get a closer initial guess, as proposed in (Ding et al 2019;Klemt et al 2019).…”

“…It limits its use to small cohorts (Handsfield et al 2014) and prevents any routine protocol. More recently, several authors proposed to use anthropometric similarities to find the closest model within a database of models extracted from MRIs and scaling it proportionally to the subject (Ding et al 2019;Klemt et al 2019).…”

An open-source musculoskeletal model of the lumbar spine and lower limbs: a validation for movements of the lumbar spine

“…Raw sEMG signals were demeaned, high-pass filtered at 30 Hz with a zerophase fourth order Butterworth filter and rectified. Rectified signals were low-pass filtered at 10 Hz (Arnold et al 2013;Klemt et al 2019). Both model activations and sEMG signals were normalised to the peak filtered value for each trial.…”

An Anatomical Atlas-Based Scaling Study for Quantifying Muscle and Hip Joint Contact Forces in Above and Through-Knee Amputees Using Validated Musculoskeletal Modelling

“…However, these do not account for interindividual anatomical variations [14]. Linear scaling to a generic model has been shown to lead to significant errors in force predictions for both upper and lower limb models [15] - [17]. Additionally, amputees have a much wider anthropometric and anatomical variability (and deficit) than able-bodied subjects and so, there are likely to be considerably greater errors in linear scaling from one subject.…”