Evidence map›Paper›PMID 24427519›Full record

ArticleInterface focus2013

Patient-specific fibre-based models of muscle wrapping.

J Kohout, G J Clapworthy, Y Zhao, Y Tao, G Gonzalez-Garcia, F Dong, H Wei, E Kohoutová

Open access · bronzeAbstract read
In one paragraph

Article in Interface focus, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.8field-weighted citation impact, top 26% of its field
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

6 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. EMG-Driven Musculoskeletal Model Calibration With Wrapping Surface Personalization.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2023
    Article
  3. Article
  4. Mechanics of Psoas Tendon Snapping. A Virtual Population Study.Frontiers in bioengineering and biotechnology · 2020
    Article
  5. Article
  6. Automated muscle segmentation from CT images of the hip and thigh using a hierarchical multi-atlas method.International journal of computer assisted radiology and surgery · 2018
    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

8 authors at 2 institutions in 2 countries.

J KohoutDepartment of Computer Science and Engineering , University of West Bohemia , Plzeň , Czech Republic.
G J ClapworthyCentre for Computer Graphics and Visualisation , University of Bedfordshire , Luton , UK.
Y ZhaoCentre for Computer Graphics and Visualisation , University of Bedfordshire , Luton , UK.
Y TaoCentre for Computer Graphics and Visualisation , University of Bedfordshire , Luton , UK.
G Gonzalez-GarciaCentre for Computer Graphics and Visualisation , University of Bedfordshire , Luton , UK.
F DongCentre for Computer Graphics and Visualisation , University of Bedfordshire , Luton , UK.
H WeiCentre for Computer Graphics and Visualisation , University of Bedfordshire , Luton , UK.
E KohoutováDepartment of Computer Science and Engineering , University of West Bohemia , Plzeň , Czech Republic.
University of Bedfordshire · GBUniversity of West Bohemia · CZ

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In many biomechanical problems, the availability of a suitable model for the wrapping of muscles when undergoing movement is essential for the estimation of forces produced on and by the body during motion. This is an important factor in the Osteoporotic Virtual Physiological Human project which is investigating the likelihood of fracture for osteoporotic patients undertaking a variety of movements. The weakening of their skeletons makes them particularly vulnerable to bone fracture caused by excessive loading being placed on the bones, even in simple everyday tasks. This paper provides an overview of a novel volumetric model that describes muscle wrapping around bones and other muscles during movement, and which includes a consideration of how the orientations of the muscle fibres change during the motion. The method can calculate the form of wrapping of a muscle of medium size and visualize the outcome within tenths of seconds on commodity hardware, while conserving muscle volume. This makes the method suitable not only for educational biomedical software, but also for clinical applications used to identify weak muscles that should be strengthened during rehabilitation or to identify bone stresses in order to estimate the risk of fractures.

Indexed as

motion fusionmuscle wrappingmusculoskeletal modellingpatient-specific modelVisualization Toolkit

Identifiers

PMID24427519
PMCPMC3638471
OpenAlexW2055640868

What OpenQuestion holds

Textmetadata
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.