An Optimization Study for the Bone-Implant Interface Performance of Lumbar Vertebral Body Cages Using a Neurogenetic Algorithm and Verification Experiment

Chian Her Lee, Ching Chi Hsu, Li Chaing

研究成果: 雜誌貢獻文章

1 引文 (Scopus)

摘要

Cage loosening continues to be a problem after spinal fusion surgery. Past studies have investigated the interfacial strength of cages in situations of a single loosening direction. However, the human spine can move in different directions; therefore, cage loosening may occur in any direction. The purposes of this study were to develop a novel finite element analysis-based neurogenetic algorithm and to discover the spike design of vertebral body cages (VBCs) with excellent interfacial pullout strength by considering multiple cage loosening directions. Five design variables of VBCs were defined, and three-dimensional finite element models were created to predict the interfacial pullout strength of different VBCs. Then, both the artificial neural network and the genetic algorithm were applied to discover the optimum VBC. Finally, one optimum VBC and five VBCs selected from the Taguchi orthogonal array were tested and compared. The optimum spike design of the VBCs was successfully determined, and the interfacial pullout strength of the optimum VBC design was superior to that of other VBCs (17–77% increase in maximum pullout force). Non-oblique spikes were suggested to enhance their loosening resistances when multiple loosening directions were considered. In conclusion, the optimum spike design revealed excellent interfacial pullout performance of vertebral body cages in multiple loosening directions. The outcome of this study could help surgeons understand the interfacial pullout strength of VBCs in terms of their spike designs, and it could provide design direction to biomechanical engineers.

原文英語
頁(從 - 到)22-32
頁數11
期刊Journal of Medical and Biological Engineering
38
發行號1
DOIs
出版狀態已發佈 - 二月 1 2018

指紋

Bone
Experiments
Surgery
Fusion reactions
Genetic algorithms
Neural networks
Finite element method
Engineers
Optimum design

ASJC Scopus subject areas

  • Biomedical Engineering

引用此文

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abstract = "Cage loosening continues to be a problem after spinal fusion surgery. Past studies have investigated the interfacial strength of cages in situations of a single loosening direction. However, the human spine can move in different directions; therefore, cage loosening may occur in any direction. The purposes of this study were to develop a novel finite element analysis-based neurogenetic algorithm and to discover the spike design of vertebral body cages (VBCs) with excellent interfacial pullout strength by considering multiple cage loosening directions. Five design variables of VBCs were defined, and three-dimensional finite element models were created to predict the interfacial pullout strength of different VBCs. Then, both the artificial neural network and the genetic algorithm were applied to discover the optimum VBC. Finally, one optimum VBC and five VBCs selected from the Taguchi orthogonal array were tested and compared. The optimum spike design of the VBCs was successfully determined, and the interfacial pullout strength of the optimum VBC design was superior to that of other VBCs (17–77{\%} increase in maximum pullout force). Non-oblique spikes were suggested to enhance their loosening resistances when multiple loosening directions were considered. In conclusion, the optimum spike design revealed excellent interfacial pullout performance of vertebral body cages in multiple loosening directions. The outcome of this study could help surgeons understand the interfacial pullout strength of VBCs in terms of their spike designs, and it could provide design direction to biomechanical engineers.",
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AB - Cage loosening continues to be a problem after spinal fusion surgery. Past studies have investigated the interfacial strength of cages in situations of a single loosening direction. However, the human spine can move in different directions; therefore, cage loosening may occur in any direction. The purposes of this study were to develop a novel finite element analysis-based neurogenetic algorithm and to discover the spike design of vertebral body cages (VBCs) with excellent interfacial pullout strength by considering multiple cage loosening directions. Five design variables of VBCs were defined, and three-dimensional finite element models were created to predict the interfacial pullout strength of different VBCs. Then, both the artificial neural network and the genetic algorithm were applied to discover the optimum VBC. Finally, one optimum VBC and five VBCs selected from the Taguchi orthogonal array were tested and compared. The optimum spike design of the VBCs was successfully determined, and the interfacial pullout strength of the optimum VBC design was superior to that of other VBCs (17–77% increase in maximum pullout force). Non-oblique spikes were suggested to enhance their loosening resistances when multiple loosening directions were considered. In conclusion, the optimum spike design revealed excellent interfacial pullout performance of vertebral body cages in multiple loosening directions. The outcome of this study could help surgeons understand the interfacial pullout strength of VBCs in terms of their spike designs, and it could provide design direction to biomechanical engineers.

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