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
Statistics
Abstract views: 169PDF downloads: 15
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Most read articles by the same author(s)
- Robert KARPIŃSKI, KNEE JOINT OSTEOARTHRITIS DIAGNOSIS BASED ON SELECTED ACOUSTIC SIGNAL DISCRIMINANTS USING MACHINE LEARNING , Applied Computer Science: Vol. 18 No. 2 (2022)
- Robert KARPIŃSKI, Przemysław KRAKOWSKI, Józef JONAK, Anna MACHROWSKA, Marcin MACIEJEWSKI, COMPARISON OF SELECTED CLASSIFICATION METHODS BASED ON MACHINE LEARNING AS A DIAGNOSTIC TOOL FOR KNEE JOINT CARTILAGE DAMAGE BASED ON GENERATED VIBROACOUSTIC PROCESSES , Applied Computer Science: Vol. 19 No. 4 (2023)
- Anna MACHROWSKA, Robert KARPIŃSKI, Marcin MACIEJEWSKI, Józef JONAK, Przemysław KRAKOWSKI, APPLICATION OF EEMD-DFA ALGORITHMS AND ANN CLASSIFICATION FOR DETECTION OF KNEE OSTEOARTHRITIS USING VIBROARTHROGRAPHY , Applied Computer Science: Vol. 20 No. 2 (2024)
- Marcin MACIEJEWSKI, Barbara MACIEJEWSKA, Robert KARPIŃSKI, Przemysław KRAKOWSKI, ELECTROCARDIOGRAM GENERATION SOFTWARE FOR TESTING OF PARAMETER EXTRACTION ALGORITHMS , Applied Computer Science: Vol. 16 No. 4 (2020)
- Anna MACHROWSKA, Robert KARPIŃSKI, Józef JONAK, Jakub SZABELSKI, NUMERICAL PREDICTION OF THE COMPONENT-RATIO-DEPENDENT COMPRESSIVE STRENGTH OF BONE CEMENT , Applied Computer Science: Vol. 16 No. 3 (2020)
- Robert KARPIŃSKI, Anna MACHROWSKA, Marcin MACIEJEWSKI, APPLICATION OF ACOUSTIC SIGNAL PROCESSING METHODS IN DETECTING DIFFERENCES BETWEEN OPEN AND CLOSED KINEMATIC CHAIN MOVEMENT FOR THE KNEE JOINT , Applied Computer Science: Vol. 15 No. 1 (2019)
- Robert KARPIŃSKI, Józef JONAK, Jacek MAKSYMIUK, MEDICAL IMAGING AND 3D RECONSTRUCTION FOR OBTAINING THE GEOMETRICAL AND PHYSICAL MODEL OF A CONGENITAL BILATERAL RADIO-ULNAR SYNOSTOSIS , Applied Computer Science: Vol. 14 No. 1 (2018)
- Anna MACHROWSKA, Robert KARPIŃSKI, Przemysław KRAKOWSKI, Józef JONAK, DIAGNOSTIC FACTORS FOR OPENED AND CLOSED KINEMATIC CHAIN OF VIBROARTHROGRAPHY SIGNALS , Applied Computer Science: Vol. 15 No. 3 (2019)
- Przemysław KRAKOWSKI, Józef JONAK, Robert KARPIŃSKI, Łukasz JAWORSKI, USEFULNESS OF RAPID PROTOTYPING IN PLANNING COMPLEX TRAUMA SURGERIES , Applied Computer Science: Vol. 15 No. 3 (2019)
- Przemysław KRAKOWSKI, Robert KARPIŃSKI, Marcin MACIEJEWSKI, APPLICATIONS OF MODERN IMAGING TECHNOLOGY IN ORTHOPAEDIC TRAUMA SURGERY , Applied Computer Science: Vol. 14 No. 3 (2018)
Similar Articles
- Nataliya SHABLIY, Serhii LUPENKO, Nadiia LUTSYK, Oleh YASNIY, Olha MALYSHEVSKA, KEYSTROKE DYNAMICS ANALYSIS USING MACHINE LEARNING METHODS , Applied Computer Science: Vol. 17 No. 4 (2021)
- Anupa ARACHCHIGE, Ranil SUGATHADASA, Oshadhi HERATH, Amila THIBBOTUWAWA, ARTIFICIAL NEURAL NETWORK BASED DEMAND FORECASTING INTEGRATED WITH FEDERAL FUNDS RATE , Applied Computer Science: Vol. 17 No. 4 (2021)
- Monika KULISZ, Aigerim DUISENBEKOVA, Justyna KUJAWSKA, Danira KALDYBAYEVA, Bibigul ISSAYEVA, Piotr LICHOGRAJ, Wojciech CEL, IMPLICATIONS OF NEURAL NETWORK AS A DECISION-MAKING TOOL IN MANAGING KAZAKHSTAN’S AGRICULTURAL ECONOMY , Applied Computer Science: Vol. 19 No. 4 (2023)
- Wulan Dewi, Wiranto Herry Utomo, PLANT CLASSIFICATION BASED ON LEAF EDGES AND LEAF MORPHOLOGICAL VEINS USING WAVELET CONVOLUTIONAL NEURAL NETWORK , Applied Computer Science: Vol. 17 No. 1 (2021)
- Mateusz Sawa, Mirosław Szala, Weronika Henzler, INNOVATIVE DEVICE FOR TENSILE STRENGTH TESTING OF WELDED JOINTS: 3D MODELLING, FEM SIMULATION AND EXPERIMENTAL VALIDATION OF TEST RIG – A CASE STUDY , Applied Computer Science: Vol. 17 No. 3 (2021)
- Muaayed F. AL-RAWI, CONVENTIONAL ENERGY EFFICIENT ROUTING PROTOCOLS IN WIRELESS SENSOR NETWORKS , Applied Computer Science: Vol. 16 No. 3 (2020)
- Md. Torikur RAHMAN, A NOVEL APPROACH TO ENHANCE THE PERFORMANCE OF MOBILE AD HOC NETWORK (MANET) THROUGH A NEW BANDWIDTH OPTIMIZATION TECHNIQUE , Applied Computer Science: Vol. 15 No. 2 (2019)
- Md. Torikur RAHMAN, Mohammad ALAUDDIN, Uttam Kumar DEY, Dr. A.H.M. Saifullah SADI, ADAPTIVE SECURE AND EFFICIENT ROUTING PROTOCOL FOR ENHANCE THE PERFORMANCE OF MOBILE AD HOC NETWORK , Applied Computer Science: Vol. 19 No. 3 (2023)
- Nawazish NAVEED, Hayan T. MADHLOOM, Mohd Shahid HUSAIN, BREAST CANCER DIAGNOSIS USING WRAPPER-BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORK , Applied Computer Science: Vol. 17 No. 3 (2021)
- Manikandan SRIDHARAN, Delphin Carolina RANI ARULANANDAM, Rajeswari K CHINNASAMY, Suma THIMMANNA, Sivabalaselvamani DHANDAPANI, RECOGNITION OF FONT AND TAMIL LETTER IN IMAGES USING DEEP LEARNING , Applied Computer Science: Vol. 17 No. 2 (2021)
You may also start an advanced similarity search for this article.
