Abstract
The wear of artificial joints, which is one of the main causes of re-implantation of the hip joint, can be minimized or entirely eliminated through a precise adjustment of artificial biobearing to individual conditions of the patient. This is possible through utilization of modern engineering tools for support of doctors in multi-aspect selection of head elements and acetabulum that work in the artificial joint. Despite a substantial number of materials used for the friction pair of both head and acetabulum, there is no perfect and universal set of biomaterials that allow for recreation of functionality of joints in all patients, while improperly selected joint articulation results in faster wear of components, migration of products of wear to soft tissue and, consequently numerous complications and necessity of repeated surgical interventions. An innovative approach that allows for customization of joint replacement, which is impossible to be achieved using conventional methods, is to utilize machine learning systems to adjust friction pair to anthropometric and goniometric characteristics of a patient. The internal elements which are used to train a fuzzy classifier ensemble are results of clinical, experimental and numerical studies that allow for prediction of the functional cycle for a patient.
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References
Arabgol, S., Ko, H.S.: Application of artificial neural network and genetic algorithm to healthcare waste prediction. Journal of Artificial Intelligence and Soft Computing Research 34(4), 243–250 (2013)
Bedzinski, R.: Technical Mechanics XII, Biomechanics. IPPT PAN (2011)
Bilski, J., Smolag, J.: Parallel architectures for learning the rtrn and elman dynamic neural networks. IEEE Transactions on Parallel and Distributed Systems PP(99), 1–1 (2014)
Bilski, J., Smoląg, J., Galushkin, A.I.: The parallel approach to the conjugate gradient learning algorithm for the feedforward neural networks. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part I. LNCS (LNAI), vol. 8467, pp. 12–21. Springer, Heidelberg (2014)
Biniaz, A., Abbasi, A.: Fast fcm with spatial neighborhood information for brain mr image segmentation. Journal of Artificial Intelligence and Soft Computing Research 3(1), 15–25 (2013)
Bruzdzinski, T., Krzyzak, A., Fevens, T., Jelen, Ł.: Ukasz Jelen: Web-based framework for breast cancer classification. Journal of Artificial Intelligence and Soft Computing Research 4(2), 149–162 (2014)
Cpałka, K.: A new method for design and reduction of neuro-fuzzy classification systems. IEEE Transactions on Neural Networks 20(4), 701–714 (2009)
Gaweda, A.E., Scherer, R.: Fuzzy number-based hierarchical fuzzy system. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 302–307. Springer, Heidelberg (2004)
Gzik, M.: Modelling and modern methods for engineering support of the locomotor system defects treatment. Publishing House of the Institute for Sustainable Technologies - PIB (2013)
Karimi, B., Krzyzak, A.: A novel approach for automatic detection and classification of suspicious lesions in breast ultrasound images. Journal of Artificial Intelligence and Soft Computing Research 3(4), 265–276 (2013)
Łapa, K., Przybył, A., Cpałka, K.: A new approach to designing interpretable models of dynamic systems. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part II. LNCS (LNAI), vol. 7895, pp. 523–534. Springer, Heidelberg (2013)
Laskowski, L.: A novel hybrid-maximum neural network in stereo-matching process. Neural Computing and Applications 23(7-8), 2435–2450 (2013)
Mallik, S., Mukhopadhyay, A., Maulik, U.: Integrated statistical and rule-mining techniques for dna methylation and gene expression data analysis. Journal of Artificial Intelligence and Soft Computing Research 3(2), 101–115 (2013)
Michnik, R.: Modeling and experimental study of human gait in terms of the rehabilitation process. Publishing House of the Institute for Sustainable Technologies - PIB (2013)
Nowicki, R.: Rough-neuro-fuzzy system with micog defuzzification. In: 2006 IEEE International Conference on Fuzzy Systems, pp. 1958–1965 (2006)
Rutkowski, L.: Computational Intelligence Methods and Techniques. Springer, Heidelberg (2008)
Schapire, R.E.: A brief introduction to boosting. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence, IJCAI 1999, vol. 2, pp. 1401–1406. Morgan Kaufmann Publishers Inc., San Francisco (1999)
Scherer, R.: Boosting ensemble of relational neuro-fuzzy systems. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 306–313. Springer, Heidelberg (2006)
Scherer, R.: Neuro-fuzzy relational systems for nonlinear approximation and prediction. Nonlinear Analysis 71, e1420–e1425 (2009)
Scherer, R.: An ensemble of logical-type neuro-fuzzy systems. Expert Systems With Applications (2011)
Scherer, R., Starczewski, J.T., Gawęda, A.: New methods for uncertainty representations in neuro-fuzzy systems. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds.) PPAM 2004. LNCS, vol. 3019, pp. 659–667. Springer, Heidelberg (2004)
Szarek, A.: Assessment of wear of metal heads in heap joint prosthesis removed from human body due to aseptic loosening. Engineering of Biomaterials 11, 6–10 (2008)
Szarek, A.: Hip Joint Replacement in Biomechanical and Clinical Approach. Belgorod, Rusnauckniga (2010)
Szarek, A.: Biomechanics and biomaterial determinants of aseptic loosening of human hip replacements. Czestochowa University of Technology Press (2015)
Szarek, A., Stradomski, G., Wlodarski, J.: The analysis of hip joint prosthesis head microstructure changes during variable stress state as a result of human motor activity. Materials Science Forum 706-709, 600–605 (2012)
Szyprowski, J.: Hip joint replacement in biomechanical and clinical approach. Oraldyn Fylym Zarsysy. Tehniceskie Nauki, Fizika, Sovremennye Informacionnye Tehnologii 36(9), 12–20 (2011)
The National Health Fund: The realization of benefits hip replacement in 2013. Technical report (2013)
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Szarek, A., Korytkowski, M., Rutkowski, L., Scherer, M., Szyprowski, J., Kostadinov, D. (2015). Customization of Joint Articulations Using Soft Computing Methods. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9120. Springer, Cham. https://doi.org/10.1007/978-3-319-19369-4_15
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DOI: https://doi.org/10.1007/978-3-319-19369-4_15
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