KNEE JOINT OSTEOARTHRITIS DIAGNOSIS BASED ON SELECTED ACOUSTIC SIGNAL DISCRIMINANTS USING MACHINE LEARNING

Robert KARPIŃSKI


Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Lublin, Poland (Poland)

Abstract

This paper presents the results of a preliminary study on simplified diagnosis of osteoarthritis of the knee joint based on generated vibroacoustic processes. The analysis was based on acoustic signals recorded in a group of 50 people, half of whom were healthy, and the other half - people with previously confirmed degenerative changes. Selected discriminants of the signals were determined and statistical analysis was performed to allow selection of optimal discriminants used at a later stage as input to the classifier. The best results of classification using artificial neural networks (ANN) of RBF (Radial Basis Function) and MLP (Multilevel Perceptron) types are presented. For the problem involving the classification of cases into one of two groups HC (Healthy Control) and OA (Osteoarthritis) an accuracy of 0.9 was obtained, with a sensitivity of 0.885 and a specificity of 0.917. It is shown that vibroacoustic diagnostics has great potential in the non-invasive assessment of damage to joint structures of the knee.


Keywords:

acoustic emission, machine learning, osteoarthritis, knee joint, kinetic chain

Ahn, J. M., & El-Khoury, G. Y. (2006). Computed Tomography of Knee Injuries. Imaging Decisions MRI, 10(1), 14–23. https://doi.org/10.1111/j.1617-0830.2006.00063.x
DOI: https://doi.org/10.1111/j.1617-0830.2006.00063.x   Google Scholar

Arendt, E. A., Miller, L. E., & Block, J. E. (2014). Early knee osteoarthritis management should first address mechanical joint overload. Orthopedic Reviews, 6(1). https://doi.org/10.4081/or.2014.5188
DOI: https://doi.org/10.4081/or.2014.5188   Google Scholar

Badurowicz, M. (2022). Detection of source code in internet texts using automatically generated machine learning models. Applied Computer Science. Applied Computer Science, 18(1), 89–98. https://doi.org/10.23743/acs-2022-07
  Google Scholar

Bauer, L., Stütz, L., & Kley, M. (2021). Black box efficiency modelling of an electric drive unit utilizing methods of machine learning. Applied Computer Science, 17(4), 5–19. https://doi.org/10.23743/acs-2021-25
DOI: https://doi.org/10.35784/acs-2021-25   Google Scholar

Będziński, R. (1997). Biomechanika inżynierska: Zagadnienia wybrane. Oficyna Wydawnicza Politechniki Wrocławskiej.
  Google Scholar

Befrui, N., Elsner, J., Flesser, A., Huvanandana, J., Jarrousse, O., Le, T. N., Müller, M., Schulze, W. H. W., Taing, S., & Weidert, S. (2018). Vibroarthrography for early detection of knee osteoarthritis using normalized frequency features. Medical & Biological Engineering & Computing, 56(8), 1499–1514. https://doi.org/10.1007/s11517-018-1785-4
DOI: https://doi.org/10.1007/s11517-018-1785-4   Google Scholar

Blodgett, W. E. (1902). Auscultation of the Knee Joint. The Boston Medical and Surgical Journal, 146(3), 63–66. https://doi.org/10.1056/NEJM190201161460304
DOI: https://doi.org/10.1056/NEJM190201161460304   Google Scholar

Brittberg, M., & Winalski, C. S. (2003). Evaluation of cartilage injuries and repair. The Journal of Bone and Joint Surgery. American Volume, 85-A Suppl 2, 58–69.
DOI: https://doi.org/10.2106/00004623-200300002-00008   Google Scholar

Cameron, M. L., Briggs, K. K., & Steadman, J. R. (2003). Reproducibility and Reliability of the Outerbridge Classification for Grading Chondral Lesions of the Knee Arthroscopically. The American Journal of Sports Medicine, 31(1), 83–86. https://doi.org/10.1177/03635465030310012601
DOI: https://doi.org/10.1177/03635465030310012601   Google Scholar

Cempel, C. (2005). Diagnostyka wibroakustyczna maszyn-historia, stan obecny, perspektywy rozwoju. Problemy Eksploatacji, 3, 7–25.
  Google Scholar

Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21(1), 6. https://doi.org/10.1186/s12864-019-6413-7
DOI: https://doi.org/10.1186/s12864-019-6413-7   Google Scholar

Cross, M., Smith, E., Hoy, D., Nolte, S., Ackerman, I., Fransen, M., Bridgett, L., Williams, S., Guillemin, F., Hill, C. L., Laslett, L. L., Jones, G., Cicuttini, F., Osborne, R., Vos, T., Buchbinder, R., Woolf, A., & March, L. (2014). The global burden of hip and knee osteoarthritis: Estimates from the Global Burden of Disease 2010 study. Annals of the Rheumatic Diseases, 73(7), 1323–1330. https://doi.org/10.1136/annrheumdis-2013-204763
DOI: https://doi.org/10.1136/annrheumdis-2013-204763   Google Scholar

Dudek-Dyduch, E., Tadeusiewicz, R., & Horzyk, A. (2009). Neural network adaptation process effectiveness dependent of constant training data availability. Neurocomputing, 72(13-15), 3138–3149. https://doi.org/10.1016/j.neucom.2009.03.017
DOI: https://doi.org/10.1016/j.neucom.2009.03.017   Google Scholar

Felson, D. T. (2004). Obesity and vocational and avocational overload of the joint as risk factors for osteoarthritis. The Journal of Rheumatology Supplement, 70, 2–5.
  Google Scholar

Figlus, T., Kozioł, M., & Kuczyński, Ł. (2019). The Effect of Selected Operational Factors on the Vibroactivity of Upper Gearbox Housings Made of Composite Materials. Sensors, 19(19), 4240. https://doi.org/10.3390/s19194240
DOI: https://doi.org/10.3390/s19194240   Google Scholar

Hayashi, D., Roemer, F. W., & Guermazi, A. (2019). Imaging of Osteoarthritis by Conventional Radiography, MR Imaging, PET–Computed Tomography, and PET-MR Imaging. PET Clinics, 14(1), 17–29. https://doi.org/10.1016/j.cpet.2018.08.004
DOI: https://doi.org/10.1016/j.cpet.2018.08.004   Google Scholar

Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1971), 903–995.
DOI: https://doi.org/10.1098/rspa.1998.0193   Google Scholar

Hunter, D. J., & Bierma-Zeinstra, S. (2019). Osteoarthritis. The Lancet, 393(10182), 1745–1759. https://doi.org/10.1016/S0140-6736(19)30417-9
DOI: https://doi.org/10.1016/S0140-6736(19)30417-9   Google Scholar

Jedliński, Ł., Caban, J., Krzywonos, L., Wierzbicki, S., & Brumerčík, F. (2015). Application of vibration signal in the diagnosis of IC engine valve clearance. Journal of Vibroengineering, 17(1), 175–187.
  Google Scholar

Jedliński, Ł., & Jonak, J. (2015). Early fault detection in gearboxes based on support vector machines and multilayer perceptron with a continuous wavelet transform. Applied Soft Computing, 30, 636–641. https://doi.org/10.1016/j.asoc.2015.02.015
DOI: https://doi.org/10.1016/j.asoc.2015.02.015   Google Scholar

Jedliński, Ł., & Jonak, J. (2020). Kontrola montazu zebatych przekladni stozkowych metoda bezdemontazowa. Wydawnictwo Politechniki Lubelskiej.
  Google Scholar

Johnson, V. L., & Hunter, D. J. (2014). The epidemiology of osteoarthritis. Best Practice & Research Clinical Rheumatology, 28(1), 5–15. https://doi.org/10.1016/j.berh.2014.01.004
DOI: https://doi.org/10.1016/j.berh.2014.01.004   Google Scholar

Jonak, J., Karpinski, R., Machrowska, A., Krakowski, P., & Maciejewski, M. (2019). A preliminary study on the use of EEMD-RQA algorithms in the detection of degenerative changes in knee joints. IOP Conference Series: Materials Science and Engineering, 710, 012037. https://doi.org/10.1088/1757-899X/710/1/012037
DOI: https://doi.org/10.1088/1757-899X/710/1/012037   Google Scholar

Karpiński, R., Krakowski, P., Jonak, J., Machrowska, A., Maciejewski, M., & Nogalski, A. (2021a). Analysis of differences in vibroacoustic signals between healthy and osteoarthritic knees using EMD algorithm and statistical analysis. Journal of Physics: Conference Series, 2130(1), 012010. https://doi.org/10.1088/1742-6596/2130/1/012010
DOI: https://doi.org/10.1088/1742-6596/2130/1/012010   Google Scholar

Karpiński, R., Krakowski, P., Jonak, J., Machrowska, A., Maciejewski, M., & Nogalski, A. (2021b). Estimation of differences in selected indices of vibroacoustic signals between healthy and osteoarthritic patellofemoral joints as a potential non-invasive diagnostic tool. Journal of Physics: Conference Series, 2130(1), 012009. https://doi.org/10.1088/1742-6596/2130/1/012009
DOI: https://doi.org/10.1088/1742-6596/2130/1/012009   Google Scholar

Karpiński, R., Krakowski, P., Jonak, J., Machrowska, A., Maciejewski, M., & Nogalski, A. (2022a). Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN-Part II: Patellofemoral Joint. Sensors, 22(10). https://doi.org/10.3390/s22103765
DOI: https://doi.org/10.3390/s22103765   Google Scholar

Karpiński, R., Krakowski, P., Jonak, J., Machrowska, A., Maciejewski, M., & Nogalski, A. (2022b). Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN-Part I: Femoral-Tibial Joint. Sensors, 22(6), 2176. https://doi.org/10.3390/s22062176
DOI: https://doi.org/10.3390/s22062176   Google Scholar

Karpiński, R., Machrowska, A., & Maciejewski, M. (2019). Application of acoustic signal processing methods in detecting differences between open and closed kinematic chain movement for the knee joint. Applied Computer Science, 15(1), 36–48. https://doi.org/10.23743/acs-2019-03
DOI: https://doi.org/10.35784/acs-2019-03   Google Scholar

Karpiński, R., Szabelski, J., & Maksymiuk, J. (2019a). Effect of Physiological Fluids Contamination on Selected Mechanical Properties of Acrylate Bone Cement. Materials, 12(23), 3963. https://doi.org/10.3390/ma12233963
DOI: https://doi.org/10.3390/ma12233963   Google Scholar

Karpiński, R., Szabelski, J., & Maksymiuk, J. (2019b). Seasoning Polymethyl Methacrylate (PMMA) Bone Cements with Incorrect Mix Ratio. Materials, 12(19), 3073. https://doi.org/10.3390/ma12193073
DOI: https://doi.org/10.3390/ma12193073   Google Scholar

Kernohan, W. G., Beverland, D. E., McCoy, G. F., Hamilton, A., Watson, P., & Mollan, R. A. B. (1990). Vibration arthrometry. Acta Orthopaedica Scandinavica, 61(1), 70–79.
DOI: https://doi.org/10.3109/17453679008993071   Google Scholar

Kosicka, E., Krzyzak, A., Dorobek, M., & Borowiec, M. (2022). Prediction of Selected Mechanical Properties of Polymer Composites with Alumina Modifiers. Materials, 15(3), 882. https://doi.org/10.3390/ma15030882
DOI: https://doi.org/10.3390/ma15030882   Google Scholar

Krakowski, P., Gerkowicz, A., Pietrzak, A., Krasowska, D., Jurkiewicz, A., Gorzelak, M., & Schwartz, R. A. (2019). Psoriatic arthritis – new perspectives. Archives of Medical Science, 15(3), 580–589. https://doi.org/10.5114/aoms.2018.77725
DOI: https://doi.org/10.5114/aoms.2018.77725   Google Scholar

Krakowski, P., Karpiński, R., Jojczuk, M., Nogalska, A., & Jonak, J. (2021). Knee MRI Underestimates the Grade of Cartilage Lesions. Applied Sciences, 11(4), 1552. https://doi.org/10.3390/app11041552
DOI: https://doi.org/10.3390/app11041552   Google Scholar

Krakowski, P., Karpiński, R., Jonak, J., & Maciejewski, R. (2021). Evaluation of diagnostic accuracy of physical examination and MRI for ligament and meniscus injuries. Journal of Physics: Conference Series, 1736, 012027. https://doi.org/10.1088/1742-6596/1736/1/012027
DOI: https://doi.org/10.1088/1742-6596/1736/1/012027   Google Scholar

Krakowski, P., Karpiński, R., Maciejewski, R., & Jonak, J. (2021). Evaluation of the diagnostic accuracy of MRI in detection of knee cartilage lesions using Receiver Operating Characteristic curves. Journal of Physics: Conference Series, 1736, 012028. https://doi.org/10.1088/1742-6596/1736/1/012028
DOI: https://doi.org/10.1088/1742-6596/1736/1/012028   Google Scholar

Krakowski, P., Karpiński, R., Maciejewski, R., Jonak, J., & Jurkiewicz, A. (2020). Short-Term Effects of Arthroscopic Microfracturation of Knee Chondral Defects in Osteoarthritis. Applied Sciences, 10(23), 8312. https://doi.org/10.3390/app10238312
DOI: https://doi.org/10.3390/app10238312   Google Scholar

Krakowski, P., Nogalski, A., Jurkiewicz, A., Karpiński, R., Maciejewski, R., & Jonak, J. (2019). Comparison of Diagnostic Accuracy of Physical Examination and MRI in the Most Common Knee Injuries. Applied Sciences, 9(19), 4102. https://doi.org/10.3390/app9194102
DOI: https://doi.org/10.3390/app9194102   Google Scholar

Krishnan, S., Rangayyan, R. M., Bell, G. D., & Frank, C. B. (2000). Adaptive time-frequency analysis of knee joint vibroarthrographic signals for noninvasive screening of articular cartilage pathology. IEEE Transactions on Biomedical Engineering, 47(6), 773–783. https://doi.org/10.1109/10.844228
DOI: https://doi.org/10.1109/10.844228   Google Scholar

Kyu, H. H., Abate, D., Abate, K. H., Abay, S. M., Abbafati, C., Abbasi, N., Abbastabar, H., Abd-Allah, F., Abdela, J., Abdelalim, A., Abdollahpour, I., Abdulkader, R. S., Abebe, M., Abebe, Z., Abil, O. Z., Aboyans, V., Abrham, A. R., Abu-Raddad, L. J., Abu-Rmeileh, N. M. E., … Murray, C. J. L. (2018). Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. The Lancet, 392(10159), 1859–1922. https://doi.org/10.1016/S0140-6736(18)32335-3
DOI: https://doi.org/10.1016/S0140-6736(18)32335-3   Google Scholar

Machrowska, A., & Jonak, J. (2018). xEMD procedures as a data—Assisted filtering method. AIP Conference Proceedings, 1922, 120007. https://doi.org/10.1063/1.5019122
DOI: https://doi.org/10.1063/1.5019122   Google Scholar

Machrowska, A., Karpiński, R., Jonak, J., Szabelski, J., & Krakowski, P. (2020). Numerical prediction of the component-ratio-dependent compressive strength of bone cement. Applied Computer Science, 16(3), 88–101. https://doi.org/10.23743/acs-2020-24
DOI: https://doi.org/10.35784/acs-2020-24   Google Scholar

Machrowska, A., Szabelski, J., Karpiński, R., Krakowski, P., Jonak, J., & Jonak, K. (2020). Use of Deep Learning Networks and Statistical Modeling to Predict Changes in Mechanical Parameters of Contaminated Bone Cements. Materials, 13(23), 5419. https://doi.org/10.3390/ma13235419
DOI: https://doi.org/10.3390/ma13235419   Google Scholar

Madej, H., Czech, P., & Konieczny, Ł. (2003). Wykorzystanie dyskryminant bezwymiarowych w diagnostyce przekładni zębatych. Diagnostyka, 28, 17–22.
  Google Scholar

Mathiessen, A., Cimmino, M. A., Hammer, H. B., Haugen, I. K., Iagnocco, A., & Conaghan, P. G. (2016). Imaging of osteoarthritis (OA): What is new? Best Practice & Research Clinical Rheumatology, 30(4), 653–669. https://doi.org/10.1016/j.berh.2016.09.007
DOI: https://doi.org/10.1016/j.berh.2016.09.007   Google Scholar

Matthews, B. W. (1975). Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA) – Protein Structure, 405(2), 442–451. https://doi.org/10.1016/0005-2795(75)90109-9
DOI: https://doi.org/10.1016/0005-2795(75)90109-9   Google Scholar

McDonough, C. M., & Jette, A. M. (2010). The Contribution of Osteoarthritis to Functional Limitations and Disability. Clinics in Geriatric Medicine, 26(3), 387–399. https://doi.org/10.1016/j.cger.2010.04.001
DOI: https://doi.org/10.1016/j.cger.2010.04.001   Google Scholar

Möller, I., Bong, D., Naredo, E., Filippucci, E., Carrasco, I., Moragues, C., & Iagnocco, A. (2008). Ultrasound in the study and monitoring of osteoarthritis. Osteoarthritis and Cartilage, 16, S4–S7. https://doi.org/10.1016/j.joca.2008.06.005
DOI: https://doi.org/10.1016/j.joca.2008.06.005   Google Scholar

Powers, D. M. W. (2020). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. ArXiv:2010.16061 [Cs, Stat]. http://arxiv.org/abs/2010.16061
  Google Scholar

Prior, J., Mascaro, B., Shark, L.-K., Stockdale, J., Selfe, J., Bury, R., Cole, P., & Goodacre, J. A. (2010). Analysis of high frequency acoustic emission signals as a new approach for assessing knee osteoarthritis. Annals of the Rheumatic Diseases, 69(5), 929–930. https://doi.org/10.1136/ard.2009.112599
DOI: https://doi.org/10.1136/ard.2009.112599   Google Scholar

Rabiej, M. (2018). Analizy statystyczne z programami Statistica i Excel. Wydawnictwo Helion.
  Google Scholar

Rangayyan, R. M., & Wu, Y. F. (2008). Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions. Medical & Biological Engineering & Computing, 46(3), 223–232. https://doi.org/10.1007/s11517-007-0278-7
DOI: https://doi.org/10.1007/s11517-007-0278-7   Google Scholar

Reyes, C., Garcia-Gil, M., Elorza, J. M., Mendez-Boo, L., Hermosilla, E., Javaid, M. K., Cooper, C., Diez-Perez, A., Arden, N. K., Bolibar, B., Ramos, R., & Prieto-Alhambra, D. (2015). Socio-economic status and the risk of developing hand, hip or knee osteoarthritis: A region-wide ecological study. Osteoarthritis and Cartilage, 23(8), 1323–1329. https://doi.org/10.1016/j.joca.2015.03.020
DOI: https://doi.org/10.1016/j.joca.2015.03.020   Google Scholar

Richette, P., & Latourte, A. (2019). Osteoarthritis: Value of imaging and biomarkers. La Revue Du Praticien, 69(5), 507–509.
  Google Scholar

Rogala, M. (2020). Neural Networks in Crashworthiness Analysis of Thin-Walled Profile with Foam Filling. Advances in Science and Technology Research Journal, 14(3), 93–99. https://doi.org/10.12913/22998624/120989
DOI: https://doi.org/10.12913/22998624/120989   Google Scholar

Stanik, Z. (2013). Diagnozowanie lozysk tocznych pojazdów samochodowych metodami wibroakustycznymi. Wydawnictwo Naukowe Instytutu Technologii Eksploatacji – Państwowego Instytutu Badawczego.
  Google Scholar

Szabelski, J. (2018). Effect of incorrect mix ratio on strength of two component adhesive Butt-Joints tested at elevated temperature. MATEC Web of Conferences, 244, 01019. https://doi.org/10.1051/matecconf/201824401019
DOI: https://doi.org/10.1051/matecconf/201824401019   Google Scholar

Szabelski, J., Karpiński, R., & Machrowska, A. (2022). Application of an Artificial Neural Network in the Modelling of Heat Curing Effects on the Strength of Adhesive Joints at Elevated Temperature with Imprecise Adhesive Mix Ratios. Materials, 15(3), 721. https://doi.org/10.3390/ma15030721
DOI: https://doi.org/10.3390/ma15030721   Google Scholar

Tadeusiewicz, R. (1993). Sieci neuronowe (Vol. 110). Akademicka Oficyna Wydawnicza.
  Google Scholar

Van den Borne, M. P. J., Raijmakers, N. J. H., Vanlauwe, J., Victor, J., de Jong, S. N., Bellemans, J., & Saris, D. B. F. (2007). International Cartilage Repair Society (ICRS) and Oswestry macroscopic cartilage evaluation scores validated for use in Autologous Chondrocyte Implantation (ACI) and microfracture. Osteoarthritis and Cartilage, 15(12), 1397–1402. https://doi.org/10.1016/j.joca.2007.05.005
DOI: https://doi.org/10.1016/j.joca.2007.05.005   Google Scholar

Walters, C. F. (1929). The value of joint auscultation. The Lancet, 213(5514), 920–921.
DOI: https://doi.org/10.1016/S0140-6736(00)79189-6   Google Scholar

Wu, Y. (2015). Knee joint vibroarthrographic signal processing and analysis. Springer.
DOI: https://doi.org/10.1007/978-3-662-44284-5   Google Scholar

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2022-06-30

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KARPIŃSKI, R. (2022). KNEE JOINT OSTEOARTHRITIS DIAGNOSIS BASED ON SELECTED ACOUSTIC SIGNAL DISCRIMINANTS USING MACHINE LEARNING. Applied Computer Science, 18(2), 71–85. https://doi.org/10.35784/acs-2022-14

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Robert KARPIŃSKI 

Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Lublin, Poland Poland

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