Abstract
Total hip joint replacement is a multi-aspect issue, where life span of the implant system in human body depends on numerous factors. One of the main reasons for having a hip replacement is loosening or wear of the associated components in artificial joint. The rate of wear depends mainly on the type of materials working together in the artificial joint, the burden resulting from the patient’s body weight, intensity of use, limb functionality, age of the patient’s and individual factors. The analysis of all factors leading to the joint wear and articulation expensiveness will allow for the appropriate selection of an head–acetabulum system which provide long-lasting and trouble-free operation. We use neuro–fuzzy systems to machine–learn the data to predict automatically the wear of elements in the artificial hip joint.
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References
Bergner, M., Bobbit, R.A., Carter, W.B., et al.: The sickness impact profile: development and final revision of a health status measure. Medical Care 19, 787–805 (1981)
Harris, W.H.: Traumatic arthritis of the hip after dislocation and acetabular fractures: treatment by mold arthroplasty. An end-result study using a new method of result evaluation. Journal Bone Joint Surg. Am. 51, 737–755 (1969)
Hwang, D.S., Kim, Y.-M., Lee, C.H.: Alumina Femoral Head Fracture in Uncemented Total Hip Arthroplasty With a Ceramic Sandwich Cup. The Journal of Arthroplasty 22(3), 468–471 (2007)
Jang, R.J.-S., Sun, C.-T., Mizutani, E.: Neuro-Fuzzy and Soft Computing. In: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, Upper Saddle River (1997)
Johnston, R.C., et al.: Clinical and radiographic evaluation of total hip replacement. A standard system of terminology for reporting results. Journal Bone Joint Surg. Am. 72, 161–168 (1990)
Merle d’Aubigone, R., Postel, M.: Functional results of hip arthroplasty with acrylic prosthese. Journal Bone Joint Surg. Br. 36-A, 451–457 (1954)
Nowicki, R.: Nonlinear modelling and classification based on the MICOG defuzzification. Nonlinear Analysis 71, e1033–e1047 (2009), doi:10.1016/j.na.2009.01.125
Nowin, S.C., Hegedus, D.H.: Bone remodeling I: theory of adaptive elasticity. Journal of Elasticity 6(3), 313–326 (1976)
Szarek, A.: Hip Joint Replacement in Biomechanical and Clinical Approach. Rusnauckniga. Belgorod (2010)
Szarek, A.: Chosen aspects of biomaterials, Publishing house Education and Science s.r.o. - Rusnauckniga, Praga-Belgorod (2011)
Ungethüm, M., Winkler-Gniewek, W.: Trybologie In der Medizin. Tribologia und Schmierungstechnik (5), 268–277 (1990)
Rutkowski, L.: Flexible Neuro Fuzzy Systems. Kluwer Academic Publishers (2004)
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Szarek, A., Korytkowski, M., Rutkowski, L., Scherer, R., Szyprowski, J. (2012). Forecasting Wear of Head and Acetabulum in Hip Joint Implant. 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_41
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DOI: https://doi.org/10.1007/978-3-642-29350-4_41
Publisher Name: Springer, Berlin, Heidelberg
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