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Research Papers

A Neurogenetic Approach to a Multiobjective Design Optimization of Spinal Pedicle Screws

[+] Author and Article Information
Ching-Kong Chao

Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei, 106, Taiwan , R.O.C

Jinn Lin

Department of Orthopedic Surgery, National Taiwan University Hospital, Taipei, 100, Taiwan, R.O.C

Sandy Tri Putra

Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei, 106, Taiwan, R.O.C

Ching-Chi Hsu1

Graduate Institute of Engineering, National Taiwan University of Science and Technology, Taipei, 106, Taiwan, R.O.Chsucc@mail.ntust.edu.tw

1

Corresponding author.

J Biomech Eng 132(9), 091006 (Aug 17, 2010) (6 pages) doi:10.1115/1.4001887 History: Received January 19, 2010; Revised May 26, 2010; Posted May 27, 2010; Published August 17, 2010; Online August 17, 2010

A pedicle screw fixation has been widely used to treat spinal diseases. Clinical reports have shown that the weakest part of the spinal fixator is the pedicle screw. However, previous studies have only focused on either screw breakage or screw loosening. There have been no studies that have addressed the multiobjective design optimization of the pedicle screws. The multiobjective optimization methodology was applied and it consisted of finite element method, Taguchi method, artificial neural networks, and genetic algorithms. Three-dimensional finite element models for both the bending strength and the pullout strength of the pedicle screw were first developed and arranged on an L25 orthogonal array. Then, artificial neural networks were used to create two objective functions. Finally, the optimum solutions of the pedicle screws were obtained by genetic algorithms. The results showed that the optimum designs had higher bending and pullout strengths compared with commercially available screws. The optimum designs of pedicle screw revealed excellent biomechanical performances. The neurogenetic approach has effectively decreased the time and effort required for searching for the optimal designs of pedicle screws and has directly provided the selection information to surgeons.

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Figures

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Figure 1

The clinical failure modes as well as the boundary and loading condition of the finite element models: (a) the bending strength models (screw breakage) and (b) the pullout strength models (screw loosening)

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Figure 2

Definition of design parameters for the spinal pedicle screw

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Figure 3

The structure of the artificial neural network

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Figure 4

The results of FEA and ANNs for learning process (from T-1 to T-25) as well as recalling process (from V-1 to V-5 and from O-1 to O-3)

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Figure 5

The results of a multiobjective optimization: (a) change in normalized objective formulae and design parameters corresponding to the given weight and (b) the trade-off solutions of the spinal pedicle screws

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