Deciphering the "Art" in Modeling and Simulation of the Knee Joint: Assessing Model Calibration Workflows and Outcomes.
Deciphering the "Art" in Modeling and Simulation of the Knee Joint: Assessing Model Calibration Workflows and Outcomes.
复制标题
解读膝关节建模和仿真中的“艺术”：评估模型校准工作流程和结果。
DOI：
10.1115/1.4063627
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
She
中科院分区：
文献类型：
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作者：
Andreassen,ThorE;Laz,PeterJ;Erdemir,Ahmet;Besier,ThorF;Halloran,JasonP;Imhauser,CarlW;Chokhandre,Snehal;Schwartz,Ariel;Nohouji,NedaAbdollahi;Rooks,NynkeB;Schneider,MarcoTY;Elmasry,Shady;Zaylor,William;Hume,DonaldR;She
模型再现性是美国国立卫生研究院（NIH）和科学界的一个重点。随着计算建模在生物力学和骨科中的应用不断增长，评估建模工作流程和模拟预测的可重复性的需求也在增长。KneeHub项目的长期目标是了解潜在主观决策的影响，因此建模者的“艺术”，对计算膝关节模型的可重复性和预测不确定性。在本文中，我们报告了该项目的模型校准阶段，在此期间，五个团队校准了来自同一个特定关节力学数据集的相同标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。校准工作流程中使用的校准目标的选择在团队之间存在很大差异，并且受到与结构表示相关的建模决策以及计算成本和优化实施考虑的影响。虽然校准提高了模型性能，但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。即使是具有专业知识的团队，模型校准也难以预见和详细规划，本研究的结果强调了确定数据共享和校准最佳做法并使其标准化的重要性。
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.