Skip to main content

Application of Neural Networks in Assessing Changes around Implant after Total Hip Arthroplasty

  • Conference paper
Artificial Intelligence and Soft Computing (ICAISC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7268))

Included in the following conference series:

Abstract

Bone and joint diseases afflict more and more younger people. This is due to the work habits, quality and intensity of life, diet and individual factors. Hip arthroplasty is a surgery to remove the pain and to allow the patient to return to normal functioning in society. Endoprosthesoplasty brings the desired effect, but the life span of contemporary endoprosthesis is still not satisfactory. Clinical studies have shown that the introduction of the implant to the bone causes a number of changes within the bone – implant contact. The correct prediction of changes around the implant allows to plan the surgery and to identify hazardous areas where bone decalcification and loss of primary stability in implant can occur.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Inc., New York (1995)

    Google Scholar 

  2. Canalis, E.: Regulation of Bone Remodeling: Primer on the Metabolic Bone Diseases and Disorders of Mineral Metabolism, An Official Publication of The American Society for Bone and Mineral Research. Amer. Society for Bone & Mineral, 33-31 (2009)

    Google Scholar 

  3. Hagan, M.T., Menhaj, M.B.: Training feed forward network with the Marquardt algorithm. IEEE Trans. on Neural Net. 5(6), 989–993 (1994)

    Article  Google Scholar 

  4. Jankowski, N., Grąbczewski, K.: Universal Meta-Learning Architecture and Algorithms. In: Jankowski, N., Duch, W., Grąbczewski, K. (eds.) Meta-Learning in Computational Intelligence. SCI, vol. 358, pp. 1–76. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  5. Marciniak, J.: Biomaterials. Silesian University of Technology Press, Gliwice (2002)

    Google Scholar 

  6. Nowicki, R., Rutkowski, L.: Rough-Neuro-Fuzzy System for Classification. In: Proceedings of Fuzzy Systems and Knowledge Discovery, Singapore, pp. 463–466 (2002)

    Google Scholar 

  7. Nowicki, R., Rutkowski, L.: Soft Techniques for Bayesian Classification. In: Rutkowski, L., Kacprzyk, J. (eds.) Neural Networks and Soft Computing. AISC, pp. 537–544. Springer Physica-Verlag (2003)

    Google Scholar 

  8. Pauwels, F.: Biomechanics of the Locomotor Apparatus, Berlin (1976)

    Google Scholar 

  9. Rutkowski, L., Cpałka, K.: A general approach to neuro - fuzzy systems. In: Proceedings of the 10th IEEE International Conference on Fuzzy Systems, Melbourne, December 2-5, vol. 3, pp. 1428–1431 (2001)

    Google Scholar 

  10. Rutkowski, L., Cpałka, K.: A neuro-fuzzy controller with a compromise fuzzy reasoning. Control and Cybernetics 31(2), 297–308 (2002)

    MATH  Google Scholar 

  11. Starczewski, J., Rutkowski, L.: Connectionist Structures of Type 2 Fuzzy Inference Systems. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds.) PPAM 2001. LNCS, vol. 2328, pp. 634–642. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  12. Starczewski, J., Rutkowski, L.: Interval type 2 neuro-fuzzy systems based on interval consequents. In: Rutkowski, L., Kacprzyk, J. (eds.) Neural Networks and Soft Computing, pp. 570–577. Physica-Verlag, Springer-Verlag Company, Heidelberg, New York (2003)

    Google Scholar 

  13. Szarek, A.: Hip Joint Replacement in Biomechanical and Clinical Approach. Rusnauckniga, Belgorod (2010)

    Google Scholar 

  14. Szarek, A.: Chosen aspects of biomaterials, Publishing house Education and Science s.r.o. - Rusnauckniga, Praga-Belgorod (2011)

    Google Scholar 

  15. Williams, D.F.: Definitions in biomaterials. Elsevier, Amsterdam (1987)

    Google Scholar 

  16. Zalasiński, M., Cpałka, K.: A new method of on-line signature verification using a flexible fuzzy one-class classifier, pp. 38–53. Academic Publishing House EXIT (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Szarek, A., Korytkowski, M., Rutkowski, L., Scherer, R., Szyprowski, J. (2012). Application of Neural Networks in Assessing Changes around Implant after Total Hip Arthroplasty. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29350-4_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-29350-4_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29349-8

  • Online ISBN: 978-3-642-29350-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics