Evidence map›Paper›PMID 30283777›Full record

ArticleFrontiers in bioengineering and biotechnology2018

An Efficient Modelling-Simulation-Analysis Workflow to Investigate Stump-Socket Interaction Using Patient-Specific, Three-Dimensional, Continuum-Mechanical, Finite Element Residual Limb Models.

Ellankavi Ramasamy, Okan Avci, Beate Dorow, Sook-Yee Chong, Leonardo Gizzi, Günter Steidle, Fritz Schick, Oliver Röhrle

Open access · goldAbstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.6field-weighted citation impact, top 18% 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

14 citing papers in PubMed, 43 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. 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 3 institutions in 1 country.

Ellankavi RamasamyDepartment of Biomechatronic Systems, Fraunhofer-Institut für Produktionstechnik und Automatisierung (Fraunhofer IPA), Stuttgart, Germany.
Okan AvciDepartment of Biomechatronic Systems, Fraunhofer-Institut für Produktionstechnik und Automatisierung (Fraunhofer IPA), Stuttgart, Germany.
Beate DorowDepartment of Biomechatronic Systems, Fraunhofer-Institut für Produktionstechnik und Automatisierung (Fraunhofer IPA), Stuttgart, Germany.
Sook-Yee ChongDiagnostische und Interventionelle Radiologie, Sektion für Experimentelle Radiologie, Department für Radiologie, Universitätsklinikum Tübingen, Tübingen, Germany.
Leonardo GizziInstitut für Mechanik (Bauwesen), Universität Stuttgart, Stuttgart, Germany.
Günter SteidleDiagnostische und Interventionelle Radiologie, Sektion für Experimentelle Radiologie, Department für Radiologie, Universitätsklinikum Tübingen, Tübingen, Germany.
Fritz SchickDiagnostische und Interventionelle Radiologie, Sektion für Experimentelle Radiologie, Department für Radiologie, Universitätsklinikum Tübingen, Tübingen, Germany.
Oliver RöhrleDepartment of Biomechatronic Systems, Fraunhofer-Institut für Produktionstechnik und Automatisierung (Fraunhofer IPA), Stuttgart, Germany.
Fraunhofer Institute for Manufacturing Engineering and Automation · DEUniversitätsklinikum Tübingen · DEUniversity of Stuttgart · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The lack of an efficient modelling-simulation-analysis workflow for creating and utilising detailed subject-specific computational models is one of the key reasons why simulation-based approaches for analysing socket-stump interaction have not yet been successfully established. Herein, we propose a novel and efficient modelling-simulation-analysis workflow that uses commercial software for generating a detailed subject-specific, three-dimensional finite element model of an entire residual limb from Diffusion Tensor MRI images in <20 min. Moreover, to complete the modelling-simulation-analysis workflow, the generated subject-specific residual limb model is used within an implicit dynamic FE simulation of bipedal stance to predict the potential sites of deep tissue injury. For this purpose, a nonlinear hyperelastic, transversely isotropic skeletal muscle constitutive law containing a deep tissue injury model was implemented in LS-DYNA. To demonstrate the feasibility of the entire modelling-simulation-analysis workflow and the fact that detailed, anatomically realistic, multi-muscle models are superior to state-of-the-art, fused-muscle models, an implicit dynamic FE analysis of 2-h bipedal stance is carried out. By analysing the potential volume of damaged muscle tissue after donning an optimally-fitted and a misfitted socket, i.e., a socket whose volume was isotropically shrunk by 10%, we were able to highlight the differences between the detailed individual- and fused-muscle models. The results of the bipedal stance simulation showed that peak stresses in the fused-muscle model were four times lower when compared to the multi-muscle model. The peak interface stress in the individual-muscle model, at the end of bipedal stance analysis, was 2.63 times lower than that in the deep tissues of the stump. At the end of the bipedal stance analysis using the misfitted socket, the fused-muscle model predicted that 7.65% of the residual limb volume was injured, while the detailed-model predicted 16.03%. The proposed approach is not only limited to modelling residual limbs but also has applications in predicting the impact of plastic surgery, for detailed forward-dynamics simulations of normal musculoskeletal systems.

Indexed as

continuum-mechanicsdiffusion tensor MRIinjuryskeletal muscletransfemoral amputation

Identifiers

PMID30283777
PMCPMC6156538
OpenAlexW2892316550

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.