APPLICATION OF NEURAL NETWORKS IN PREDICTION OF TENSILE STRENGTH OF ABSORBABLE SUTURES
Robert KARPIŃSKI
r.karpinski@pollub.plLublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin (Poland)
Jakub GAJEWSKI
Lublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin, (Poland)
Jakub SZABELSKI
Lublin University of Technology, Faculty of Mechanical Engineering, Institute of Technological Systems of Information, Nadbystrzycka 36, 20-618 Lublin (Poland)
Dalibor BARTA
University of Zilina, Faculty of Mechanical Engineering, Univerzitna 1, 01026 Zilina (Slovakia)
Abstract
The paper presents results of research on neural network application in forecasting the tensile strength of two types of sutures. The preliminary research was conducted in order to establish the accuracy of the proposed method and will be used for formulating further research areas. The neural network enabled evaluation of suture material degradation after 3-to-6-days’ exposure to Ringer’s solution. The encountered problems regarding inaccuracies show that developing a single model for sutures may be difficult or impossible. Therefore future research should be conducted for a single type of sutures only and require applying additional parameters for the neural network.
Keywords:
neural network application, forecasting, sutures tensileReferences
Bollom, T., & Meister, K. (2013). Surgical principles: biodegradable materials in sports Medicine. In J. C. DeLee, D. J. Drez, & M. D. Miller (Eds.), DeLee & Drez's Orthopaedic Sports Medicine: Principles and Practice. 2nd edition. Philadelphia, PA: Saunders.
Google Scholar
Casey, D. J., & Lewis, O.G. (1986). Absorbable and nonabsorbable sutures. In A.F. von Recum (Ed.), Handbook of biomaterials. Scientific and clinical testing of implant materials. New York: Macmillan.
Google Scholar
Gajewski, J., Golewski, P., & Sadowski, T. (2017). Geometry optimization of a thin-walled element for an air structure using hybrid system integrating artificial neural network and finite element method. Composite Structures, 159, pp. 589–599. https://doi.org/10.1016/j.compstruct.2016.10.007
DOI: https://doi.org/10.1016/j.compstruct.2016.10.007
Google Scholar
Hasnaoui, H., Krea, M., & Roizard, D. (2017). Neural networks for the prediction of polymer permeability to gases. Journal of Membrane Science, 541, 541–549. https://doi.org/10.1016/j.memsci.2017.07.031
DOI: https://doi.org/10.1016/j.memsci.2017.07.031
Google Scholar
Karpiński, R., Górniak, B., Szabelski, J., & Szala, M. (2016b). Charakterystyka i podział materiałów szewnych, In B. Zdunek, & M. Szklarczyk (Eds.), Wybrane zagadnienia z biologii molekularnej oraz inżynierii materiałowej (pp. 127–139). Lublin: Wydawnictwo Naukowe TYGIEL Sp. z. o. o.
Google Scholar
Karpiński, R., Górniak, B., Szabelski, J., & Szala, M. (2016a). Historia chirurgii i materiałów szewnych, In B. Zdunek, & M. Szklarczyk (Eds.), Wybrane zagadnienia z biologii molekularnej oraz inżynierii materiałowej (pp. 140–150). Lublin: Wydawnictwo Naukowe TYGIEL Sp. z. o. o.
Google Scholar
Karpiński, R., Szabelski, J., & Maksymiuk, J. (2017). Effect of Ringer's Solution on Tensile Strength of Non-Absorbable, Medium- and Long-Term Absorbable Sutures. Advances in Science and Technology Research Journal, 11(4), 11-20. https://doi.org/10.12913/22998624/76084
DOI: https://doi.org/10.12913/22998624/76084
Google Scholar
Krysicki, W., Bartos, J., Dyczka, W., Królikowska, K., & Wasilewski, M. (1999). Rachunek prawdopodobieństwa i statystyka matematyczna w zadaniach. część II. Statystyka matematyczna. Wydanie Szóste. Warszawa: Wydawnictwo Naukowe PWN.
Google Scholar
Lv, H., & Zheng, Y. (2017). A newly developed tridimensional neural network for prediction of the phase equilibria of six aqueous two-phase systems. Journal of Industrial and Engineering Chemistry, 57, 377–386. https://doi.org/10.1016/j.jiec.2017.08.046
DOI: https://doi.org/10.1016/j.jiec.2017.08.046
Google Scholar
Rabiej, M. (2012). Statystyka z programem Statistica. Gliwice: Helion.
Google Scholar
Youshia, J., Ali, M. E., & Lamprecht, A. (2017). Artificial neural network based particle size prediction of polymeric nanoparticles. European Journal of Pharmaceutics and Biopharmaceutics, 119, 333–342. https://doi.org/10.1016/j.ejpb.2017.06.030
DOI: https://doi.org/10.1016/j.ejpb.2017.06.030
Google Scholar
Luo, Y. (2017). Recurrent neural networks for classifying relations in clinical notes. Journal of Biomedical Informatics, 72, 85–95.
DOI: https://doi.org/10.1016/j.jbi.2017.07.006
Google Scholar
Zapalski, S., & Chęciński, P. (1999). Szwy chirurgiczne: wybrane problemy. Bielsko-Biała: AlfaMedica Press.
Google Scholar
Zurek, M., Kajzer, A., Basiaga, M., & Jendruś, R. (2016). Właściwości wytrzymałościowe wybranych polimerowych nici chirurgicznych. Polimery, 61 (5), 334–338. https://doi.org/10.14314/polimery.2016.334
DOI: https://doi.org/10.14314/polimery.2016.334
Google Scholar
Authors
Robert KARPIŃSKIr.karpinski@pollub.pl
Lublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin Poland
Authors
Jakub GAJEWSKILublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin, Poland
Authors
Jakub SZABELSKILublin University of Technology, Faculty of Mechanical Engineering, Institute of Technological Systems of Information, Nadbystrzycka 36, 20-618 Lublin Poland
Authors
Dalibor BARTAUniversity of Zilina, Faculty of Mechanical Engineering, Univerzitna 1, 01026 Zilina Slovakia
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