Multi-objective design optimization of dental implant geometrical parameters

In-silico investigations are becoming an integral part of the development of novel biomedical devices, including dental implants. Using computer simulations can streamline the process by tuning different geometrical and structural features, emphasizing the osseointegration of the implant design a priori, leading to the optimal designs in preparation for in-vivo trails. This research aims to elucidate the interrelationship between 12 geometrical variables that holistically define the shape of the implant. The approach to achieve optimality hinged on coupling the finite element analysis results with the fractional factorial design method. The latter was used to determine the most influential variables during the screening process, followed by the parameter optimization process using the response surface method, regarding four different objectives, namely: bone-implant contact area, volume of trabecular bone dead cells, volume of cortical bone dead cells, and axial displacement. This resulted in reducing the number of virtual experiments and substantially decreasing the computational cost without compromising the accuracy of the solution. It was found that the optimized values improved the performance significantly. The validity of all models was verified by comparing optimized responses with simulation results. A sensitivity analysis was performed on all five optimized models to address the effect of friction coefficient on the implant-bone joint interaction. It was shown that the mechanical behavior of implant-bone would be independent in higher friction coefficients. The significance of this study is demonstrated in determining the most effective and optimized values of all possible geometrical parameters considering their singular or interactive effects.

Automated summary

In this study, computer simulations were used to optimize the design of dental implants by understanding the relationship between geometric variables, reducing computational costs, and improving performance. The combination of finite element analysis and fractional factorial design method led to significant improvements in design optimization.