Esrafilian Amir, Stenroth Lauri, Mononen Mika E, Vartiainen Paavo, Tanska Petri, Karjalainen Pasi A, Suomalainen Juha-Sampo, Arokoski J, Saxby David J, Lloyd David G, Korhonen Rami K
IEEE Trans Biomed Eng. 2022 Sep;69(9):2860-2871. doi: 10.1109/TBME.2022.3156018. Epub 2022 Aug 19.
Joint tissue mechanics (e.g., stress and strain) are believed to have a major involvement in the onset and progression of musculoskeletal disorders, e.g., knee osteoarthritis (KOA). Accordingly, considerable efforts have been made to develop musculoskeletal finite element (MS-FE) models to estimate highly detailed tissue mechanics that predict cartilage degeneration. However, creating such models is time-consuming and requires advanced expertise. This limits these complex, yet promising, MS-FE models to research applications with few participants and makes the models impractical for clinical assessments. Also, these previously developed MS-FE models have not been used to assess activities other than gait. This study introduces and verifies a semi-automated rapid state-of-the-art MS-FE modeling and simulation toolbox incorporating an electromyography- (EMG) assisted MS model and a muscle-force driven FE model of the knee with fibril-reinforced poro(visco)elastic cartilages and menisci. To showcase the usability of the pipeline, we estimated joint- and tissue-level knee mechanics in 15 KOA individuals performing different daily activities. The pipeline was verified by comparing the estimated muscle activations and joint mechanics to existing experimental data. To determine the importance of the EMG-assisted MS analysis approach, results were compared to those from the same FE models but driven by static-optimization-based MS models. The EMG-assisted MS-FE pipeline bore a closer resemblance to experiments compared to the static-optimization-based MS-FE pipeline. Importantly, the developed pipeline showed great potential as a rapid MS-FE analysis toolbox to investigate multiscale knee mechanics during different activities of individuals with KOA.
关节组织力学(如应力和应变)被认为在肌肉骨骼疾病(如膝关节骨关节炎,KOA)的发病和进展中起主要作用。因此,人们已经做出了相当大的努力来开发肌肉骨骼有限元(MS - FE)模型,以估计能够预测软骨退变的高度详细的组织力学。然而,创建这样的模型既耗时又需要先进的专业知识。这使得这些复杂但有前景的MS - FE模型仅限于少数参与者的研究应用,并且使模型在临床评估中不切实际。此外,这些先前开发的MS - FE模型尚未用于评估除步态以外的活动。本研究介绍并验证了一种半自动的先进MS - FE建模与仿真工具箱,该工具箱包含一个肌电图(EMG)辅助的MS模型以及一个带有纤维增强多孔(粘弹性)软骨和半月板的膝关节肌肉力驱动有限元模型。为了展示该流程的可用性,我们估计了15名进行不同日常活动的KOA个体的关节和组织水平的膝关节力学。通过将估计的肌肉激活和关节力学与现有实验数据进行比较来验证该流程。为了确定EMG辅助的MS分析方法的重要性,将结果与来自相同有限元模型但由基于静态优化的MS模型驱动的结果进行了比较。与基于静态优化的MS - FE流程相比,EMG辅助的MS - FE流程与实验结果更为相似。重要的是,所开发的流程作为一种快速的MS - FE分析工具箱,在研究KOA个体不同活动期间的多尺度膝关节力学方面显示出巨大潜力。