Computational Biomechanics Lab, Mechanical and Materials Engineering, University of Denver, Denver, CO, USA.
J Biomech. 2011 Jan 4;44(1):13-21. doi: 10.1016/j.jbiomech.2010.08.016. Epub 2010 Sep 9.
Many aspects of biomechanics are variable in nature, including patient geometry, joint mechanics, implant alignment and clinical outcomes. Probabilistic methods have been applied in computational models to predict distributions of performance given uncertain or variable parameters. Sensitivity analysis is commonly used in conjunction with probabilistic methods to identify the parameters that most significantly affect the performance outcome; however, it does not consider coupled relationships for multiple output measures. Principal component analysis (PCA) has been applied to characterize common modes of variation in shape and kinematics. In this study, a novel, combined probabilistic and PCA approach was developed to characterize relationships between multiple input parameters and output measures. To demonstrate the benefits of the approach, it was applied to implanted patellofemoral (PF) mechanics to characterize relationships between femoral and patellar component alignment and loading and the resulting joint mechanics. Prior studies assessing PF sensitivity have performed individual perturbation of alignment parameters. However, the probabilistic and PCA approach enabled a more holistic evaluation of sensitivity, including identification of combinations of alignment parameters that most significantly contributed to kinematic and contact mechanics outcomes throughout the flexion cycle, and the predictive capability to estimate joint mechanics based on alignment conditions without requiring additional analysis. The approach showed comparable results for Monte Carlo sampling with 500 trials and the more efficient Latin Hypercube sampling with 50 trials. The probabilistic and PCA approach has broad applicability to biomechanical analysis and can provide insight into the interdependencies between implant design, alignment and the resulting mechanics.
生物力学的许多方面在本质上都是可变的，包括患者的几何形状、关节力学、植入物的对准以及临床结果。概率方法已应用于计算模型中，以预测给定不确定或可变参数的性能分布。敏感性分析通常与概率方法一起用于确定对性能结果影响最大的参数；然而，它不考虑多个输出测量的耦合关系。主成分分析（PCA）已被用于描述形状和运动学的常见变化模式。在这项研究中，开发了一种新颖的、概率和 PCA 相结合的方法来描述多个输入参数和输出测量之间的关系。为了展示该方法的优势，将其应用于植入式髌股（PF）力学中，以描述股骨和髌骨组件对准与加载以及由此产生的关节力学之间的关系。先前评估 PF 敏感性的研究对对准参数进行了单独的扰动。然而，概率和 PCA 方法能够更全面地评估敏感性，包括确定最显著影响整个弯曲周期运动学和接触力学结果的对准参数组合，以及基于对准条件预测关节力学的能力，而无需进行额外的分析。该方法在蒙特卡罗采样 500 次和更有效的拉丁超立方采样 50 次的情况下均具有可比的结果。概率和 PCA 方法具有广泛的生物力学分析适用性，可以深入了解植入物设计、对准和由此产生的力学之间的相互依赖关系。
