Evidence map›Paper›PMID 30932815›Full record

ArticleIEEE transactions on bio-medical engineering2019

Improving Musculoskeletal Model Scaling Using an Anatomical Atlas: The Importance of Gender and Anthropometric Similarity to Quantify Joint Reaction Forces.

Ziyun Ding, Chui K Tsang, Daniel Nolte, Angela E Kedgley, Anthony M J Bull

Abstract read
In one paragraph

Article in IEEE transactions on bio-medical engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ziyun Ding
Chui K Tsang
Daniel Nolte
Angela E Kedgley
Anthony M J Bull

Funding

Wellcome Trust 208858
6 · The paper itself

Abstract

objectiveThe accuracy of a musculoskeletal model relies heavily on the implementation of the underlying anatomical dataset. Linear scaling of a generic model, despite being time and cost efficient, produces substantial errors as it does not account for gender differences and inter-individual anatomical variations. The hypothesis of this study is that linear scaling to a musculoskeletal model with gender and anthropometric similarity to the individual subject produces similar results to the ones that can be obtained from a subject-specific model.

methodsA lower limb musculoskeletal anatomical atlas was developed consisting of ten datasets derived from magnetic resonance imaging of healthy subjects and an additional generic dataset from the literature. Predicted muscle activation and joint reaction force were compared with electromyography and literature data. Regressions based on gender and anthropometry were used to identify the use of atlas.

resultsPrimary predictors of differences for the joint reaction force predictions were mass difference for the ankle (p < 0.001) and length difference for the knee and hip (p ≤ 0.017). Gender difference accounted for an additional 3% of the variance (p ≤ 0.039). Joint reaction force differences at the ankle, knee, and hip were reduced by between 50% and 67% (p = 0.005) when using a musculoskeletal model with the same gender and similar anthropometry in comparison with a generic model.

conclusionLinear scaling with gender and anthropometric similarity can improve joint reaction force predictions in comparison with a scaled generic model. SIGNIFICANCE: The presented scaling approach and atlas can improve the fidelity and utility of musculoskeletal models for subject-specific applications.

Indexed as

Hip JointKnee JointModels, AnatomicMuscle, SkeletalAdultAnthropometryBiomechanical PhenomenaElectromyographyFemaleHumansMagnetic Resonance ImagingMaleYoung Adult

Identifiers

PMID30932815
PMCPMC7100011

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.