title: 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 L25 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.
authors: Department of Mechanical Engineering, National Taiwan University of Science and Technology 43, Section 4 Keelung Road, Taipei 106, Taiwan.
authors: Yongyut
authors: Y
authors: Amaritsakul
authors: Ching-Kong
authors: CK
authors: Chao
authors: Jinn
authors: J
authors: Lin
mesh_terms: D000465:Algorithms; D001696:Biomechanical Phenomena; D001863:Bone Screws / Q000706:statistics & numerical data; D020342:Finite Element Analysis; D005593:Fracture Fixation, Internal / Q000295:instrumentation; D006801:Humans; D021621:Imaging, Three-Dimensional; D016268:Internal Fixators / Q000706:statistics & numerical data; D008422:Materials Testing; D016571:Neural Networks, Computer; D011474:Prosthesis Design / Q000379:methods* / Q000706:statistics & numerical data; D016103:Spinal Fractures / Q000601:surgery*; D013314:Stress, Mechanical
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