Optimization of pedicle screw position using finite element method and neural networks

The application of pedicle screws in instrumentation of the spine is widely used in spine correction surgeries. High stresses can be developed in the region surrounding the screw, and it is important to understand how to minimize this stress in order to reduce risk of patient harm. Traditional studies have used FEM (finite element method) to evaluate the best position and orientation of the pedicle screws; however, such approach demands iterative simulations to find the minimum stress position. This work has used an optimization-based method, based on neural network and genetic algorithm, to test different pedicular screw positions. By performing simulations of different screw positions through finite element analysis and using the results to train an artificial neural network, a less computationally intensive method of determining stress was found. The mathematical model created was optimized through a genetic algorithm and used to find a trajectory that results in lower mechanical stress between the pedicle screw and the vertebra. The results of the ANN approach show a reduction of 5.25% in the von Mises stress when compared to the FEA. Regarding the optimal trajectory, the angles differences were 2° in sagittal plane and 1.4° in transverse plane, hence showing good results.