Multiobjective Optimization Design of Spinal Pedicle Screws Using Neural Networks and Genetic Algorithm: Mathematical Models and 
Mechanical Validation

Abstract

Short-segment instrumentation for spine fractures is threatened by relatively high failure rates. Failure of the spinal pedicle screws 
including breakage and loosening may jeopardize the fixation integrity and lead to treatment failure. Two important design objectives, 
bending strength and pullout strength, may conflict with each other and warrant a multiobjective optimization study. In the present 
study using the three-dimensional finite element (FE) analytical results based on an L-25 orthogonal array, bending and pullout objective 
functions were developed by an artificial neural network (ANN) algorithm, and the trade-off solutions known as Pareto optima were explored 
by a genetic algorithm(GA). The results showed that the knee solutions of the Pareto fronts with both high bending and pullout strength 
ranged from 92% to 94% of their maxima, respectively. In mechanical validation, the results of mathematical analyses were closely related 
to those of experimental tests with a correlation coefficient of -0.91 for bending and 0.93 for pullout (P < 0.01 for both). The optimal 
design had significantly higher fatigue life (P < 0.01) and comparable pullout strength as compared with commercial screws. Multiobjective 
optimization study of spinal pedicle screws using the hybrid of ANN and GA could achieve an ideal with high bending and pullout performances 
simultaneously.