Evidence map›Paper›PMID 42756198›Full record

ArticleFrontiers in bioengineering and biotechnology2026

Evidence-informed methodological approaches for coupled neuromusculoskeletal-finite element analysis workflows in orthopaedic biomechanics.

Alireza Y Bavil, Ayda Karimi Dastgerdi, Rod S Barrett, Bradley M Cornish, Matthew J Hambly, Matthew Carrall-Worsey, Claudio Pizzolato, Amir Esrafilian, Dale Robinson, Laurent Frossard and 4 more

Abstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

14 authors.

Alireza Y BavilSchool of Mechanical Engineering and Industrial Design, Griffith University, Gold Coast, QLD, Australia.
Ayda Karimi DastgerdiAustralian Centre for Precision Health and Technology (PRECISE), Griffith University, Gold Coast, QLD, Australia.
Rod S BarrettAustralian Centre for Precision Health and Technology (PRECISE), Griffith University, Gold Coast, QLD, Australia.
Bradley M CornishAustralian Centre for Precision Health and Technology (PRECISE), Griffith University, Gold Coast, QLD, Australia.
Matthew J HamblyAustralian Centre for Precision Health and Technology (PRECISE), Griffith University, Gold Coast, QLD, Australia.
Matthew Carrall-WorseyAustralian Centre for Precision Health and Technology (PRECISE), Griffith University, Gold Coast, QLD, Australia.
Claudio PizzolatoAustralian Centre for Precision Health and Technology (PRECISE), Griffith University, Gold Coast, QLD, Australia.
Amir EsrafilianAustralian Centre for Precision Health and Technology (PRECISE), Griffith University, Gold Coast, QLD, Australia.
Dale RobinsonSchool of Mechanical and Mining Engineering, University of Queensland, Brisbane, QLD, Australia.
Laurent FrossardAustralian Centre for Precision Health and Technology (PRECISE), Griffith University, Gold Coast, QLD, Australia.
David John SaxbyAustralian Centre for Precision Health and Technology (PRECISE), Griffith University, Gold Coast, QLD, Australia.
Stefanie FeihSchool of Mechanical Engineering and Industrial Design, Griffith University, Gold Coast, QLD, Australia.
Christopher P CartyAustralian Centre for Precision Health and Technology (PRECISE), Griffith University, Gold Coast, QLD, Australia.
David G LloydAustralian Centre for Precision Health and Technology (PRECISE), Griffith University, Gold Coast, QLD, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Orthopaedic surgical outcomes remain variable due to the poorly characterised, patient-specific post-surgical mechanical and physiological environment of implants and tissues. Differences in anatomy, alignment, neuromuscular function, and movement govern surgical-site loading and the tissue-level environment that drive healing. Coupled neuromusculoskeletal-finite element analysis (NMSK-FEA) workflows link these factors to the tissue-level mechanical and physiological environment, enabling preoperative evaluation of surgical strategies. This paper outlines current methodological considerations for the development and application of coupled NMSK-FEA workflows in orthopaedic biomechanics and identifies key priorities for their translation into clinically useful decision-support tools. We discuss methodological considerations and rationale for creating personalised MSK and NMSK models, patient-specific FEA geometry and material definition, transfer of NMSK outputs into FEA motion, loading, and boundary conditions, and selection of clinically relevant output measures. The workflow enforces NMSK and FEA parameter and boundary condition constraints to generate physiologically plausible outputs. Case studies of anterior cruciate ligament reconstruction and proximal femoral osteotomy are presented as methodological exemplars to illustrate how these NMSK-FEA workflow recommendations can be operationalised. These examples of NMSK-FEA workflows used in clinical cases show that mechanically optimal solutions can differ substantially between patients, thereby reinforcing the need for personalised biomechanical assessment. This paper presents practical recommendations for standardising and validating workflows and communicating key findings in a format that supports clinical decision making, laying the groundwork for prospective clinical validation. Together, these recommendations help move orthopaedic surgery planning beyond population averages towards patient-specific decision support at the point of care.

Indexed as

digital twinfinite element analysisneuromusculoskeletal modellingorthopaedic biomechanicssurgical planning and simulation

Identifiers

PMID42756198
PMCPMC13582369

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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.