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Improvement in the Process of Designing a New Artificial Human Intervertebral Lumbar Disc Combining Soft Computing Techniques and the Finite Element Method

  • Rubén Lostado LorzaEmail author
  • Fátima Somovilla Gomez
  • Roberto Fernandez Martinez
  • Ruben Escribano Garcia
  • Marina Corral Bobadilla
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 527)

Abstract

Human intervertebral lumbar disc degeneration is painful and difficult to treat, and is often magnified when the patient is overweight. When the damage is excessive, the disc is replaced by a non-natural or artificial disc. Artificial discs sometimes have the disadvantage of totally different behavior from that of the natural disc. This affects substantially the quality of treated patient’s life. The Finite Element Method (FEM) has been used for years to design an artificial disc, but it involves a high computational cost. This paper proposes a methodology to design a new Artificial Human Intervertebral Lumbar Disc by combining FEM and soft computing techniques. Firstly, a three-dimensional Finite Element (FE) model of a healthy disc was generated and validated experimentally from cadavers by standard tests. Then, an Artificial Human Intervertebral Lumbar Disc FE model with a core of Polycarbonate Polyurethane (PCU) was modeled and parameterized. The healthy and artificial disc FE models were both assembled between lumbar vertebrae L4-L5, giving place to the Functional Spinal Unit (FSU). A Box-Behnken Design of Experiment (DoE) was generated that considers the parameters that define the geometry of the proposed artificial disc FE model and the load derived from the patient’s height and body weight. Artificial Neural Networks (ANNs) and regression trees that are based on heuristic methods and evolutionary algorithms were used for modeling the compression and lateral bending stiffness from the FE simulations of the artificial disc. In this case, ANNs proved to be the models that had the best generalization ability. Finally, the best geometry of the artificial disc proposed when the patient’s height and body weight were considered was achieved by applying Genetic Algorithms (GA) to the ANNs. The difference between the compression and lateral bending stiffness obtained from the healthy and artificial discs did not differ significantly. This indicated that the proposed methodology provides a powerful tool for the design and optimization of an artificial prosthesis.

Keywords

Finite elements method Data mining techniques Genetic algorithms Biomechanics Design of artificial intervertebral lumbar disc 

Notes

Acknowledgements

The authors wish to thank the University of the Basque Country UPV/EHU for its support through Project US15/18 OMETESA and the University of La Rioja for its support through Project ADER 2014-I-IDD-00162.

References

  1. 1.
    Lee, C.K., Goel, V.K.: Artificial disc prosthesis: design concepts and criteria. Spine J. 4(6), S209–S218 (2004)CrossRefGoogle Scholar
  2. 2.
    Van den Broek, P.R., Huyghe, J.M., Wilson, W., Ito, K.: Design of next generation total disk replacements. J. Biomech. 45(1), 134–140 (2012)CrossRefGoogle Scholar
  3. 3.
    Lostado, R., Martinez, R.F., Mac Donald, B.J., Villanueva, P.M.: Combining soft computing techniques and the finite element method to design and optimize complex welded products. Integr. Comput. Aided Eng. 22(2), 153–170 (2015)CrossRefGoogle Scholar
  4. 4.
    Gomez, F.S., Lorza, R.L., Martinez, R.F., Bobadilla, M.C., Garcia, R.E.: A proposed methodology for setting the finite element models based on healthy human intervertebral lumbar discs. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds.) HAIS 2016. LNCS, vol. 9648, pp. 621–633. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-32034-2_52CrossRefGoogle Scholar
  5. 5.
    Hsu, C.C.: Shape optimization for the subsidence resistance of an interbody device using simulation-based genetic algorithms and experimental validation. J. Orthop. Res. 31(7), 1158–1163 (2013)CrossRefGoogle Scholar
  6. 6.
    Belytschko, T., Kulak, R.F., Schultz, A.B., Galante, J.O.: Finite element stress analysis of an intervertebral disc. J. Biomech. 7, 276–285 (1974)CrossRefGoogle Scholar
  7. 7.
    Panjabi, M.M., Brand, R.A., White, A.A.: Mechanical properties of the human thoracic spine. J. Bone Joint Surg. Am. 58(5), 642–652 (1976)CrossRefGoogle Scholar
  8. 8.
    Hoffler, C.E., Moore, K.E., Kozloff, K., Zysset, P.K., Goldstein, S.A.: Age, gender, and bone lamellae elastic moduli. J. Orthop. Res. 18(3), 432–437 (2000)CrossRefGoogle Scholar
  9. 9.
    Chen, H., Zhou, X., Fujita, H., Onozuka, M., Kubo, K.Y.: Age-related changes in trabecular and cortical bone microstructure. Int. J. Endocrinol. 3 (2013)Google Scholar
  10. 10.
    Tsouknidas, A., Michailidis, N., Savvakis, S., Anagnostidis, K., Bouzakis, K.D., Kapetanos, G.: A finite element model technique to determine the mechanical response of a lumbar spine segment under complex loads. J. Appl. Biomech. 28(4), 448–456 (2012)CrossRefGoogle Scholar
  11. 11.
    R Core Team: R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2013). http://www.R-project.org/
  12. 12.
    Fernandez, R., Okariz, A., Ibarretxe, J., Iturrondobeitia, M., Guraya, T.: Use of decision tree models based on evolutionary algorithms for the morphological classification of reinforcing nano-particle aggregates. Comput. Mater. Sci. 92, 102–113 (2014)CrossRefGoogle Scholar
  13. 13.
    Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, New York (1996)CrossRefGoogle Scholar
  14. 14.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth International Group, Belmont (1984)zbMATHGoogle Scholar
  15. 15.
    Grubinger, T., Zeileis A., Pfeiffer K.P.: evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R. Research Platform Empirical and Experimental Economics, Universitt Innsbruck (2011)Google Scholar
  16. 16.
    Schwarz, G.: Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978)MathSciNetCrossRefGoogle Scholar

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Authors and Affiliations

  • Rubén Lostado Lorza
    • 1
    Email author
  • Fátima Somovilla Gomez
    • 1
  • Roberto Fernandez Martinez
    • 2
  • Ruben Escribano Garcia
    • 3
  • Marina Corral Bobadilla
    • 1
  1. 1.Mechanical Engineering DepartmentUniversity of La RiojaLogroñoSpain
  2. 2.Electrical Engineering DepartmentUniversity of Basque CountryBilbaoSpain
  3. 3.Built Environment and EngineeringLeeds Beckett UniversityLeedsUK

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