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 chainReferences
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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Robert KARPIŃSKIDepartment of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Lublin, Poland Poland
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