Evidence map›Paper›PMID 42238375›Full record

ArticleOsteoarthritis imaging2024

Recent developments in computational modelling of the knee.

Kaiwen Yang, Marcus G Pandy

Abstract read
In one paragraph

Article in Osteoarthritis imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

2 authors.

Kaiwen YangDepartment of Mechanical Engineering, University of Melbourne, Parkville, Victoria 3101, Australia.
Marcus G PandyDepartment of Mechanical Engineering, University of Melbourne, Parkville, Victoria 3101, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Model calculations of knee joint loading range from an assumption of perfectly rigid articular surfaces to more realistic simulations of cartilage and meniscal deformation. Rigid-body musculoskeletal models simulate knee contact mechanics using the 'bed of springs' method from elastic foundation theory whereas finite-element models discretise each structure into a series of interconnected elements and ascribe material properties to each element. This mini-review describes some of the most recent developments in computational modelling of knee contact mechanics and suggests possible avenues for future improvements. Design: Narrative mini-review. Results: Muscle and joint contact forces can be calculated synchronously at a reasonable computational cost (typically a few hours of CPU time) using rigid-body models and elastic foundation theory whereas similar calculations using fully deformable finite-element models can take several days and even weeks. The main computational expense incurred in finite-element musculoskeletal modelling is the solution of a muscle-force optimization problem. Conclusion: Calculation of muscle and joint contact forces within the framework of a finite-element musculoskeletal model remains challenging. Integrating biomechanical data from human motion experiments with fully deformable finite-element models to simulate knee contact mechanics during dynamic activity is an evolving science. Future work should explore the use of efficient methods such as direct collocation to perform muscle-driven dynamic optimization simulations of movement using finite-element musculoskeletal models. Dynamic optimization may be combined with finite-element modelling to enable predictive simulations of movement so that the effects of changes in musculoskeletal anatomy on knee contact mechanics can be studied more systematically.

Indexed as

Elastic foundation knee modelFinite-element knee modelJoint contactMuscle force optimization

Identifiers

PMID42238375
PMCPMC13228703

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Registered trials

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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.