Elwell Josie A, Athwal George S, Willing Ryan
Department of Mechanical Engineering, Thomas J. Watson School of Engineering and Applied Science, State University of New York, Binghamton, New York.
Roth McFarlane Hand and Upper Limb Centre, London, Ontario, Canada.
J Orthop Res. 2018 Dec;36(12):3308-3317. doi: 10.1002/jor.24131. Epub 2018 Sep 19.
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.
对严重依赖肌肉活动的手术结果（即关节置换）进行评估和优化，需要一个能够在自动环境中准确可靠地预测肌肉路径的模型。在本研究中，针对解剖学肩部以及植入反向全肩关节置换术（rTSA）的肩部开发并验证了这样一个模型，因为这些情况呈现出特别复杂的运动范围和包裹几何形状。一个有限元（FE）模型包括四个肩袖肌肉和三个三角肌束的“珍珠串”表示。肌肉束由15个通过线性弹性弹簧连接并在其起点附着于骨骼的刚性球体组成。将肌肉束的自由端拉至其止点，之后对肩部施加运动。计算肌肉力臂，并与文献中的数据进行定性比较，同时使用皮尔逊相关系数和均方根误差。在植入rTSA后重复该过程。FE模型在不到7分钟的时间内捕捉了整个180°运动过程中的肌肉路径。在30°和60°肩外展时的力臂通常落在先前实验和计算研究预测的范围内。FE模型在rTSA前后的外展、屈曲和轴向旋转方面与先前发表的结果显示出良好的定性一致性。总之，开发并验证了一个能够在存在可变包裹几何形状的情况下预测肌肉路径的模型，且没有牺牲足够的计算效率以至于无法在诸如设计优化等数值技术中使用。© 2018年骨科研究协会。由威利期刊公司出版。《矫形外科学研究》36:3308 - 3317，2018年。
