Evidence map›Paper›PMID 32598483›Full record

Trial reportJournal of anatomy2020

Subject-specific muscle properties from diffusion tensor imaging significantly improve the accuracy of musculoskeletal models.

James P Charles, Barbara Grant, Kristiaan D'Août, Karl T Bates

Abstract readClinical Trial
In one paragraph

Trial report in Journal of anatomy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

24 citing papers in PubMed.

  1. Article
  2. Article
  3. Benchmarking Neural Network Personalized Musculoskeletal Hand Models Against Current Personalization Standards Using Experimental Magnetic Resonance Imaging and Fine-Wire Electromyography.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. From fibre to function: are we accurately representing muscle architecture and performance?Biological reviews of the Cambridge Philosophical Society · 2022
    Review
  19. Skeletal muscle function underpins muscle spindle abundance.Proceedings. Biological sciences · 2022
    Article
  20. Article
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

4 authors.

James P CharlesDepartment of Musculoskeletal and Ageing Science , Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK.ORCID 0000-0001-8256-8035
Barbara GrantDepartment of Musculoskeletal and Ageing Science , Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK.
Kristiaan D'AoûtDepartment of Musculoskeletal and Ageing Science , Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK.
Karl T BatesDepartment of Musculoskeletal and Ageing Science , Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK.

Funding

Medical Research Council MR/P020941/1
6 · The paper itself

Abstract

Musculoskeletal modelling is an important platform on which to study the biomechanics of morphological structures in vertebrates and is widely used in clinical, zoological and palaeontological fields. The popularity of this approach stems from the potential to non-invasively quantify biologically important but difficult-to-measure functional parameters. However, while it is known that model predictions are highly sensitive to input values, it is standard practice to build models by combining musculoskeletal data from different sources resulting in 'generic' models for a given species. At present, there are little quantitative data on how merging disparate anatomical data in models impacts the accuracy of these functional predictions. This issue is addressed herein by quantifying the accuracy of both subject-specific human limb models containing individualised muscle force-generating properties and models built using generic properties from both elderly and young individuals, relative to experimental muscle torques obtained from an isokinetic dynamometer. The results show that subject-specific models predict isokinetic muscle torques to a greater degree of accuracy than generic models at the ankle (root-mean-squared error - 7.9% vs. 49.3% in elderly anatomy-based models), knee (13.2% vs. 57.3%) and hip (21.9% vs. 32.8%). These results have important implications for the choice of musculoskeletal properties in future modelling studies, and the relatively high level of accuracy achieved in the subject-specific models suggests that such models can potentially address questions about inter-subject variations of muscle functions. However, despite relatively high levels of overall accuracy, models built using averaged generic muscle architecture data from young, healthy individuals may lack the resolution and accuracy required to study such differences between individuals, at least in certain circumstances. The results do not wholly discourage the continued use of averaged generic data in musculoskeletal modelling studies but do emphasise the need for to maximise the accuracy of input values if studying intra-species form-function relationships in the musculoskeletal system.

Indexed as

Diffusion Tensor ImagingPatient-Specific ModelingAdultAged, 80 and overFemaleHumansMaleMuscle, SkeletalTorquebiomechanical modellingfibre lengthsmagnetic resonance imagingmuscle torquesmuscle volumes

Identifiers

PMID32598483
PMCPMC7542200

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.