Computational intelligence based design of implant for varying bone conditions.

ABSTRACT

The objective is to make the strain deviation before and after implantation adjacent to the femoral implant as close as possible to zero. Genetic algorithm is applied for this optimization of strain deviation, measured in eight separate positions. The concept of composite desirability is introduced in such a way that if the microstrain deviation values for all eight cases are 0, then the composite desirability is 1. Artificial neural network (ANN) models are developed to capture the correlation of the microstrain in femur implants using the data generated through finite element simulation. Then, the ANN model is used as the surrogate model, which in combination with the desirability function serves as the objective function for optimization. The optimum achievable deviation was found to vary with the bone condition. The optimum implant geometry varied for different bone condition, and the findings act as guideline for designing patient-specific implant.