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Abstract：
Model reproducibility is a point of emphasis for the National Institutes of Health (NIH) and in science, broadly. As the use of computational modeling in biomechanics and orthopedics grows, so does the need to assess the reproducibility of modeling workflows and simulation predictions. The long-term goal of the KneeHub project is to understand the influence of potentially subjective decisions, thus the modeler\'s \"art\", on the reproducibility and predictive uncertainty of computational knee joint models. In this paper, we report on the model calibration phase of this project, during which five teams calibrated computational knee joint models of the same specimens from the same specimen-specific joint mechanics dataset. We investigated model calibration approaches and decisions, and compared calibration workflows and model outcomes among the teams. The selection of the calibration targets used in the calibration workflow differed greatly between the teams and was influenced by modeling decisions related to the representation of structures, and considerations for computational cost and implementation of optimization. While calibration improved model performance, differences in the postcalibration ligament properties and predicted kinematics were quantified and discussed in the context of modeling decisions. Even for teams with demonstrated expertise, model calibration is difficult to foresee and plan in detail, and the results of this study underscore the importance of identification and standardization of best practices for data sharing and calibration.
摘要：
模型的可重复性是美国国立卫生研究院（NIH）和科学的重点，广义上。随着计算模型在生物力学和骨科中的应用越来越多，评估建模工作流程和模拟预测的可重复性的需求也是如此。KneeHub项目的长期目标是了解潜在主观决策的影响，因此，建模师的“艺术”，关于计算膝关节模型的可重复性和预测不确定性。在本文中,我们报告了这个项目的模型校准阶段，在此期间，五个团队从相同的样本特定的关节力学数据集中校准了相同样本的计算膝关节模型。我们研究了模型校准方法和决策，并比较了团队之间的校准工作流程和模型结果。在校准工作流程中使用的校准目标的选择在团队之间差异很大，并受到与结构表示相关的建模决策的影响，以及对计算成本和优化实施的考虑。虽然校准提高了模型性能，在建模决策的背景下,对校准后韧带特性和预测运动学的差异进行了量化和讨论.即使对于具有专业知识的团队，模型校准很难详细预见和计划，这项研究的结果强调了识别和标准化数据共享和校准最佳实践的重要性。
