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

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.