Predicting the hip joint centre in children: New regression equations, linear scaling, and statistical shape modelling

Carman, Laura; Besier, Thor F.; Choisne, Julie

doi:10.1016/j.jbiomech.2022.111265

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…Additionally, using the pediatric statistical shape model decreased the error in hip joint center prediction compared with linear scaling and regression analysis. 52 Using the same data set, we found that the Euclidian distance error in hip joint center was, on average, 3 mm for the statistical shape model, 5.45 mm using the linear scaling method, and 6.24 mm when using regression equations. Our pediatric statistical shape model had lower error than our adult shape model, 53 which was not unexpected given the variance of pediatric bone morphology compared to an adult cohort.…”

Section: Predicting Pediatric Bone Morphology Using Partial Least Squ...mentioning

confidence: 81%

A Narrative Review of Personalized Musculoskeletal Modeling Using the Physiome and Musculoskeletal Atlas Projects

Fernandez,

Shim,

Schneider

et al. 2023

Journal of Applied Biomechanics

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In this narrative review, we explore developments in the field of computational musculoskeletal model personalization using the Physiome and Musculoskeletal Atlas Projects. Model geometry personalization; statistical shape modeling; and its impact on segmentation, classification, and model creation are explored. Examples include the trapeziometacarpal and tibiofemoral joints, Achilles tendon, gastrocnemius muscle, and pediatric lower limb bones. Finally, a more general approach to model personalization is discussed based on the idea of multiscale personalization called scaffolds.

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Section: Predicting Pediatric Bone Morphology Using Partial Least Squ...mentioning

confidence: 81%

A Narrative Review of Personalized Musculoskeletal Modeling Using the Physiome and Musculoskeletal Atlas Projects

Fernandez,

Shim,

Schneider

et al. 2023

Journal of Applied Biomechanics

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“…This was performed by manual selection of nodes from only the template mesh for the femoral head and neck to form a point cloud for geometric fitting. Fitting a sphere to the femoral head is a common method for accurate determination of the femoral head centre [71], [82], [170]. Cylinder fitting of the femoral neck is less common as often 3d geometry is not used for measurement, however this was determined to be the best approach from both manual inspection of the proximal femoral axis and existing literature [171], [172].…”

Section: Angular and Torsional Measurementsmentioning

confidence: 99%

“…The following chapter is published as "Predicting the hip joint centre in children: New regression equations, linear scaling, and statistical shape modelling" in the Journal of Biomechanics (https://doi.org/10.1016/j.jbiomech.2022.111265). Authored by Laura Carman, Thor Besier, and Julie Choisne [170].…”

Section: Chapter 5 -Predicting the Hip Joint Centre In Childrenmentioning

confidence: 99%

Modelling the Form and Function of the Paediatric Musculoskeletal System

Carman

2024

Preprint

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Musculoskeletal development in a paediatric population is an important but poorly understood domain. Understanding how bone geometry changes with growth, allows development of more accurate models in a paediatric population. Which can be used for applications such as musculoskeletal and finite element modelling, to help understand information about movement including joint contact and muscle forces. Utilising a dataset of 333 CT scans of children aged 4 – 18 years old, this thesis explores changes in musculoskeletal geometry with growth. This thesis evaluates clinical bone measurement changes with growth. Three-dimensional angular and linear measurements from the pelvis, femur, and tibia/fibula were extracted. Notably, femoral and tibial torsion remained consistent between sexes, while sex-related differences in linear measurements emerged in children aged 13 and older. Females consistently displayed smaller linear measurements compared to males of the same age/height, possibly contributing to increased injury risks during adolescence. Accurate estimation of the hip joint centre is vital for assessing hip joint motion and related parameters. In this thesis, three different hip joint centre determination methods were evaluated in a paediatric population—regression equations, linear scaling, and shape model prediction. Our novel shape model prediction exhibited superior accuracy compared to alternative methods. Addressing the challenge of rapidly generating musculoskeletal geometry in a paediatric population, this thesis presents population-based shape models for lower limb bones. These models utilised principal component analysis on segmented pelvis, femur, and tibia/fibula data, to accurately predict bone geometry from demographic and linear bone measurements. Subsequent articulation of the shape model allowed for both shape and pose prediction of lower limb bones from bone surface landmarks. The generated shape models outperformed traditional linear scaling methods, offering a more accurate and accessible solution for paediatric musculoskeletal geometry prediction. Overall, this thesis underscores the importance of understanding changes in musculoskeletal geometry with growth and provides valuable tools and methods which capture these changes. This thesis not only illustrates sex-related skeletal differences but has also pioneered the use of statistical shape models in a paediatric population to predict lower limb musculoskeletal geometry. These findings hold significant implications for clinical and research applications, advancing our understanding of paediatric musculoskeletal geometry.

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“…The knee and ankle joint centers can be estimated using functional methods or as the midpoint between the medial and lateral femoral condyles and malleoli 18,21 . Linear scaling of the pelvis can be improved by incorporating hip joint center (HJC) estimations 22,23 , but these HJC locations are difficult to estimate because they cannot be directly identified from surface marker locations. The HJC locations are commonly estimated using either functional estimation methods or regression equations.…”

mentioning

confidence: 99%

Assessing Scaling and Kinematic Errors in a Coupled Experimental-Computational Infant Musculoskeletal Model

Chambers,

Walck,

Huayamave

et al. 2023

10th Edition of the International Conference on Computational Methods for Coupled Problems in Science and Engineering

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Musculoskeletal models are valuable tools that enable the study and quantification of biomechanical parameters, allowing researchers to better understand the mechanisms influencing or contributing to human movement. Furthermore, musculoskeletal models have the potential to serve as diagnostic tools for identifying pathologies and disorders, such as developmental dysplasia of the hip. However, current musculoskeletal models are developed using adult subjects, with only a few studies focusing on infant populations, despite the greatest growth rate being in early infancy. Therefore, the objective of this study was to evaluate the impact of multiple linear scaling approaches of increasing complexity on the development of an infant musculoskeletal model. Motion capture technology was used to collect data from the spontaneous kicking movement of a 2.4-month-old infant lying supine. The experimental motion capture data and anthropometric measurements were used to scale the generic gait2392 OpenSim model. Four linear scaling methods of increasing complexity were used: uniform (Uni), nonuniform (Non), nonuniform with knee and ankle joint centers (NAKJCs), and nonuniform with knee, ankle, and regression-derived hip joint centers (NHJCs). Results suggest that the maximum marker errors decreased with the increasing complexity of the scaling approach. The Uni scaling approach resulted in the largest scaling and kinematic errors, with maximum marker errors of 4.92 cm and 5.30 cm, respectively. The NHJCs scaling approach had the lowest maximum marker errors, with errors of 4.17 cm and 4.36 cm, respectively. The scaling method used to develop infant musculoskeletal models should be considered carefully, especially when using linearly scaling generic models developed using adult cadaveric data. INTRODUCTIONMusculoskeletal modeling has exploded in biomechanics research, with OpenSim 1 being the most common open-source tool. Musculoskeletal models are developed to quantify

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Predicting the hip joint centre in children: New regression equations, linear scaling, and statistical shape modelling

Cited by 9 publications

References 21 publications

A Narrative Review of Personalized Musculoskeletal Modeling Using the Physiome and Musculoskeletal Atlas Projects

A Narrative Review of Personalized Musculoskeletal Modeling Using the Physiome and Musculoskeletal Atlas Projects

Modelling the Form and Function of the Paediatric Musculoskeletal System

Assessing Scaling and Kinematic Errors in a Coupled Experimental-Computational Infant Musculoskeletal Model

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