Peyraut A, Genet M
Solid Mechanics Laboratory, École Polytechnique/IPP/CNRS, Palaiseau 91120, France; MΞDISIM Team, INRIA, Palaiseau 91120, France.
Laboratoire Mécanique des Solides.
J Biomech Eng. 2025 Aug 1;147(8). doi: 10.1115/1.4068578.
The development of personalized models is a key step for addressing various problems, especially in biomechanics. These models typically include many constants, introduced in the model material law or loading definition, and their estimation is crucial for the model personalization. However, performing solely the estimation does not yield any information on the estimation accuracy. Additionally, all parameters can typically not be estimated based only on clinical data: some parameters are identified, while others are fixed at generic values. The question of the identifiability of the parameters, along with the robustness of the estimation, notably to measurement errors and to model errors, is therefore crucial and should be quantitatively addressed in parallel to the model development. In this paper, we propose a general inverse uncertainty quantification pipeline based on the creation of synthetic data-for which the parameters ground-truth values are known-generated for different noise and model error levels. Estimation is then performed for many realizations of the noise or model errors, as well as parameter initializations, until convergence of the estimated parameters error distributions. This pipeline was applied to a poromechanical lung model for illustration and validation purposes. It provides quantitative information on the actual identifiability of the parameters, and any derived quantity of interest, in the clinical setting. In particular, it allows us to retrieve a confidence interval for each estimated parameter, which represents valuable information for diagnosis or prognosis use of the estimated values. This work is therefore a step toward improving the reliability of digital twins pipelines.
个性化模型的开发是解决各种问题的关键步骤，尤其是在生物力学领域。这些模型通常包括许多在模型材料定律或载荷定义中引入的常数，其估计对于模型个性化至关重要。然而，仅进行估计并不能提供关于估计准确性的任何信息。此外，通常不能仅基于临床数据估计所有参数：一些参数是识别出来的，而其他参数则固定为通用值。因此，参数的可识别性问题以及估计的稳健性，尤其是对测量误差和模型误差的稳健性，至关重要，并且应该在模型开发的同时进行定量处理。在本文中，我们提出了一种基于合成数据创建的通用逆不确定性量化管道——对于这些合成数据，已知参数的真实值——针对不同的噪声和模型误差水平生成。然后针对噪声或模型误差的许多实现以及参数初始化进行估计，直到估计参数误差分布收敛。为了说明和验证目的，该管道被应用于一个孔隙力学肺模型。它提供了关于临床环境中参数实际可识别性以及任何感兴趣的派生量的定量信息。特别是，它使我们能够为每个估计参数检索一个置信区间，这对于估计值的诊断或预后使用代表了有价值的信息。因此，这项工作是朝着提高数字双胞胎管道的可靠性迈出的一步。
