Evaluate Medical Device Design Robustness by Combining Statistical and Probabilistic Tools with Finite Element Analysis

Finite Element Analysis (FEA) has been widely used to analyze the in vivo structural response of implantable medical devices and assess device performance, reliability and durability. Take stents for example. Several characteristics like radial force, peak stresses and strains and fatigue safety factors can be determined using these modeling techniques. As all physical phenomena are stochastic, all design parameters have built-in variability. On the other hand, stents are typically modeled with nominal dimensions and material properties. Minimum and maximum dimensions are used in certain instances, while other dimensional variances are often not considered. In addition, the in vivo boundary conditions also vary widely across patient population. Therefore, in order to obtain a robust design, parameter variations and their influence on device performance need to be understood. Probabilistic analysis techniques like Monte Carlo sampling combined with FEA are helpful in understanding these stochastic processes and in determining probability of failure and design reliability. In this study, FEA using Abaqus is combined with statistical and probabilistic analysis using Isight to demonstrate how these techniques work together to evaluate the robustness of medical device designs.