Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran; Structural Integrity & Composites, Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS, Delft, Netherlands.
Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
J Mech Behav Biomed Mater. 2022 Jun;130:105210. doi: 10.1016/j.jmbbm.2022.105210. Epub 2022 Apr 1.
IntraOcular Pressure (IOP) is one of the most informative factors for monitoring the eye-health. This is usually measured by tonometers. However, the outputs of the tonometers depend on the physical and geometrical properties of the cornea. Therefore, the common practice is to develop a numerical model to generate some correction factors. The main challenge here is the accuracy and efficiency of a numerical model in predicting the IOP and Dynamic Corneal Response (DCR) of each patient. This study addresses this issue by developing a two-step surrogate model based on adaptive sparse Polynomial Chaos Expansion (PCE) for fast and accurate prediction of the IOP. In this regard, first, an FE model of the cornea has been developed to predict the DCR parameters. This FE model has been replaced with a PCE-based surrogate model to speed up the simulation step. The uncertainties in the geometry and material model of the cornea have been propagated through the surrogate model to estimate the distributions of the DCR parameters. In the second step, the combination of DCR parameters and the input parameters provide a proper parameter space for developing an efficient data-driven PCE model to predict the IOP. Moreover, sensitivity analysis by using PCE-based Sobol indices has been performed. The results demonstrate the accuracy and efficiency of the proposed method in predicting the IOP. Sensitivity analysis revealed that IOP measurement was influenced mostly by deflection amplitude and applanation time. The analysis indicates the importance of the interactions between the parameters.
眼压（IOP）是监测眼睛健康的最具信息量的因素之一。这通常通过眼压计来测量。然而，眼压计的输出结果取决于角膜的物理和几何特性。因此，通常的做法是开发数值模型来生成一些校正因子。这里的主要挑战是数值模型在预测每个患者的眼压和动态角膜响应（DCR）方面的准确性和效率。本研究通过开发基于自适应稀疏多项式混沌扩展（PCE）的两步代理模型来解决这个问题，以快速准确地预测眼压。在这方面，首先，开发了角膜的有限元模型来预测 DCR 参数。这个有限元模型已经被基于 PCE 的代理模型所取代，以加快模拟步骤。通过代理模型传播了角膜的几何形状和材料模型中的不确定性，以估计 DCR 参数的分布。在第二步中，DCR 参数和输入参数的组合为开发有效的数据驱动 PCE 模型提供了合适的参数空间，以预测眼压。此外，还进行了基于 PCE 的 Sobol 指数的敏感性分析。结果表明，该方法在预测眼压方面具有准确性和高效性。敏感性分析表明，眼压测量受挠度幅度和压平时间的影响最大。分析表明了参数之间相互作用的重要性。
