Abstract
Assessment and optimization of procedural outcomes, namely joint replacement, that rely heavily on muscle action necessitates a model capable of accurately and reliably predicting muscle paths in an automated setting. In this study, such a model was developed and validated for the anatomic shoulder and one implanted with reverse total shoulder arthroplasty (rTSA), as these scenarios present particularly complex ranges of motion and wrapping geometries. A finite element (FE) element model included a "string-of-pearls" representation of the four rotator cuff muscles and the three deltoid muscle bundles. Muscle bundles consisted of 15 rigid spheres connected by linearly elastic springs and attached to the bones at their origins. The free ends of the muscle bundles were pulled to their insertions, after which motions were applied to the shoulder. Muscle moment arms were calculated and compared to data available in the literature qualitatively and using Pearson rho values and root-mean-square errors. The process was repeated following implantation of an rTSA. The FE model captured muscle paths throughout 180° of motion in under seven minutes. Moment arms at 30° and 60° of scaption generally fell within the ranges predicted by previous experimental and computational studies. The FE model showed good qualitative agreement with previously published results for abduction, flexion, and axial rotation before and after rTSA. In conclusion, a model capable of predicting muscle paths in the presence of variable wrapping geometry was developed and validated without sacrificing enough computational efficiency to render its use impossible in numerical techniques such as design optimization. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:3308-3317, 2018.