Download PDFOpen PDF in browserA validated, patient-specific, muscle mapping model of the shoulder7 pages•Published: December 17, 2024AbstractPatient-specific computational models of the shoulder have the potential to further our understanding of joint biomechanics and optimise treatments for common pathologies such as osteoarthritis and rotator cuff tears. Since active motion and stability of the shoulder are mainly governed by the surrounding soft tissues, such models must be able to reliable and accurately predict muscle paths. Our aim was to develop and validate a computationally efficient line-segment muscle mapping model capable of mapping patient-specific muscle paths of the multi-pennate muscles of the deltoid and rotator cuff.A triangular surface mesh was generated from segmentations of an anonymous male subject (75 years old, BMI 23) and muscle origin/insertion points were identified based upon previously reported data. A ‘convex hull’ algorithm identified optimal muscle fibre paths during 0-90° coronal plane abduction, sagittal plane flexion and axial rotation at neutral elevation. The model was capable of computing muscle lengths, moment arms and line of action for each muscle fibre. The model had acceptable correlation with in vivo and cadaveric data from the literature. The model was also highly efficient, capable of mapping 42 muscle segments at 2.5° intervals of joint motion in less than 17 seconds. The current model presents a patient specific method for modelling muscle multi-pennate muscles of the shoulder with high computational efficiency, requiring only the surface mesh inputs of the bony anatomy and muscle origin/insertion points without the need for commercial finite element contact detection software. The model may be used further for the study of shoulder musculoskeletal disorders. Keyphrases: fe model, line segment, muscle mapping, musculoskeletal, patient specific, shoulder In: Joshua W Giles and Aziliz Guezou-Philippe (editors). Proceedings of The 24th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 7, pages 160-166.
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