COMPARISON OF SELECTED CLASSIFICATION METHODS BASED ON MACHINE LEARNING AS A DIAGNOSTIC TOOL FOR KNEE JOINT CARTILAGE DAMAGE BASED ON GENERATED VIBROACOUSTIC PROCESSES
Robert KARPIŃSKI
r.karpinski@pollub.plDepartment of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, (Poland)
https://orcid.org/0000-0003-4063-8503
Przemysław KRAKOWSKI
Medical University of Lublin, Chair and Department of Traumatology and Emergency Medicine, Staszica 11, 20-081 Lublin, Poland, przemyslawkrakowski@umlub.pl, Orthopaedic and Sports Traumatology Department, Carolina Medical Center, Pory 78, 02-757, Warsaw, Poland (Poland)
Józef JONAK
Lublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin, Poland, (Poland)
Anna MACHROWSKA
Lublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin, Poland, (Poland)
Marcin MACIEJEWSKI
Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Institute of Electronics and Information Technology, Nadbystrzycka 36, 20-618 Lublin, Poland, (Poland)
Abstract
Osteoarthritis is one of the most common cause of disability among elderly. It can affect every joint in human body, however, it is most prevalent in hip, knee, and hand joints. Early diagnosis of cartilage lesions is essential for fast and accurate treatment, which can prolong joint function. Available diagnostic methods include conventional X-ray, ultrasound and magnetic resonance imaging. However, those diagnostic modalities are not suitable for screening purposes. Vibroarthrography is proposed in literature as a screening method for cartilage lesions. However, exact method of signal acquisition as well as classification method is still not well established in literature. In this study, 84 patients were assessed, of whom 40 were in the control group and 44 in the study group. Cartilage status in the study group was evaluated during surgical treatment. Multilayer perceptron - MLP, radial basis function - RBF, support vector method - SVM and naive classifier – NBC were introduced in this study as classification protocols. Highest accuracy (0.893) was found when MLP was introduced, also RBF classification showed high sensitivity (0.822) and specificity (0.821). On the other hand, NBC showed lowest diagnostic accuracy reaching 0.702. In conclusion vibroarthrography presents a promising diagnostic modality for cartilage evaluation in clinical setting with the use of MLP and RBF classification methods.
Keywords:
articular cartilage, Artificial Intelligence, RBF, MLP, SVM, knee jointReferences
Andersen, R. E., Arendt-Nielsen, L., & Madeleine, P. (2016). A review of engineering aspects of vibroarthography of the knee joint. Critical Reviews in Physical and Rehabilitation Medicine, 28(1–2), 13–32. https://doi.org/10.1615/CritRevPhysRehabilMed.2016017185
DOI: https://doi.org/10.1615/CritRevPhysRehabilMed.2016017185
Google Scholar
Ashoorion, V., Sadeghirad, B., Wang, L., Noori, A., Abdar, M., Kim, Y., Chang, Y., Rehman, N., Lopes, L. C., Couban, R. J., Aminilari, M., Malektojari, A., Ghazizadeh, S., Rehman, Y., Ghasemi, M., Adili, A., Guyatt, G. H., & Busse, J. W. (2023). Predictors of persistent post-surgical pain following total knee arthroplasty: A systematic review and meta-analysis of observational studies. Pain Medicine, 24(4), 369–381. https://doi.org/10.1093/pm/pnac154
DOI: https://doi.org/10.1093/pm/pnac154
Google Scholar
Aziz, N., Akhir, E. A. P., Aziz, I. A., Jaafar, J., Hasan, M. H., & Abas, A. N. C. (2020). A study on gradient boosting algorithms for development of AI monitoring and prediction systems. 2020 International Conference on Computational Intelligence (ICCI) (pp. 11–16). IEEE. https://doi.org/10.1109/ICCI51257.2020.9247843
DOI: https://doi.org/10.1109/ICCI51257.2020.9247843
Google Scholar
Barnett, A. J., & Toms, A. D. (2012). Revision total hip and knee replacement. Clinics in Geriatric Medicine, 28(3), 431-446. https://doi.org/10.1016/j.cger.2012.05.008
DOI: https://doi.org/10.1016/j.cger.2012.05.008
Google Scholar
Bennasar, M., Setchi, R., Hicks, Y., & Bayer, A. (2014). Cascade classification for diagnosing dementia. 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 2535–2540). IEEE. https://doi.org/10.1109/SMC.2014.6974308
DOI: https://doi.org/10.1109/SMC.2014.6974308
Google Scholar
Bose, B. K. (2007). Neural network applications in power electronics and motor drives - An introduction and perspective. IEEE Transactions on Industrial Electronics, 54(1), 14–33. https://doi.org/10.1109/TIE.2006.888683
DOI: https://doi.org/10.1109/TIE.2006.888683
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, 6. https://doi.org/10.1186/s12864-019-6413-7
DOI: https://doi.org/10.1186/s12864-019-6413-7
Google Scholar
Chih-Wei, H., & Chih-Jen, L. (2002). A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, 13(2), 415–425. https://doi.org/10.1109/72.991427
DOI: https://doi.org/10.1109/72.991427
Google Scholar
Emadi Andani, M., & Salehi, Z. (2024). An affordable and easy-to-use tool to diagnose knee arthritis using knee sound. Biomedical Signal Processing and Control, 88, 105685. https://doi.org/10.1016/j.bspc.2023.105685
DOI: https://doi.org/10.1016/j.bspc.2023.105685
Google Scholar
Figueroa, D., Calvo, R., Vaisman, A., Carrasco, M. A., Moraga, C., & Delgado, I. (2007). Knee chondral lesions: incidence and correlation between arthroscopic and magnetic resonance findings. Arthroscopy: The Journal of Arthroscopic & Related Surgery, 23(3), 312-315. https://doi.org/10.1016/j.arthro.2006.11.015
DOI: https://doi.org/10.1016/j.arthro.2006.11.015
Google Scholar
Ghahramani, Z., & Kim, H. C. (2003). Bayesian classifier combination. Gatsby Computational Neuroscience Unit University College London.
Google Scholar
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
Google Scholar
Huang, Y., & Li, L. (2011). Naive Bayes classification algorithm based on small sample set. 2011 IEEE International Conference on Cloud Computing and Intelligence Systems (pp. 34–39). IEEE. https://doi.org/10.1109/CCIS.2011.6045027
DOI: https://doi.org/10.1109/CCIS.2011.6045027
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. (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
DOI: https://doi.org/10.35784/acs-2022-14
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, 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, 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 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., Krakowski, P., Jonak, J., Machrowska, A., Maciejewski, M., & Nogalski, A. (2022b). Diagnostics of articular cartilage damage based on generated acoustic signals using ANN - Part II: Patellofemoral joint. Sensors, 22(10), 3765. https://doi.org/10.3390/s22103765
DOI: https://doi.org/10.3390/s22103765
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
Kotsiantis, S. B. (2013). Decision trees: A recent overview. Artificial Intelligence Review, 39, 261–283. https://doi.org/10.1007/s10462-011-9272-4
DOI: https://doi.org/10.1007/s10462-011-9272-4
Google Scholar
Krakowski, P., Karpiński, R., Jojczuk, M., Nogalska, A., & Jonak, J. (2021a). 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. (2021b). 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. (2021c). 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
Lemon, S. C., Roy, J., Clark, M. A., Friedmann, P. D., & Rakowski, W. (2003). Classification and regression tree analysis in public health: Methodological review and comparison with logistic regression. Annals of Behavioral Medicine, 26(3), 172–181. https://doi.org/10.1207/S15324796ABM2603_02
DOI: https://doi.org/10.1207/S15324796ABM2603_02
Google Scholar
Liu, R., Yang, B., Zio, E., & Chen, X. (2018). Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 108, 33–47. https://doi.org/10.1016/j.ymssp.2018.02.016
DOI: https://doi.org/10.1016/j.ymssp.2018.02.016
Google Scholar
Luque, A., Carrasco, A., Martín, A., & De Las Heras, A. (2019). The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognition, 91, 216–231. https://doi.org/10.1016/j.patcog.2019.02.023
DOI: https://doi.org/10.1016/j.patcog.2019.02.023
Google Scholar
Łysiak, A., Froń, A., Bączkowicz, D., & Szmajda, M. (2020). Vibroarthrographic signal spectral features in 5-class knee joint classification. Sensors, 20(17), 5015. https://doi.org/10.3390/s20175015
DOI: https://doi.org/10.3390/s20175015
Google Scholar
Machrowska, A., Karpiński, R., Jonak, J., Szabelski, J., & Krakowski, P. (2020a). 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., Karpiński, R., Krakowski, P., & Jonak, J. (2019). Diagnostic factors for opened and closed kinematic chain of vibroarthrography signals. Applied Computer Science, 15(3), 34-44. https://doi.org/10.23743/acs-2019-19
DOI: https://doi.org/10.35784/acs-2019-19
Google Scholar
Machrowska, A., Szabelski, J., Karpiński, R., Krakowski, P., Jonak, J., & Jonak, K. (2020b). 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
Meng Joo Er, Shiqian Wu, Juwei Lu, & Hock Lye Toh. (2002). Face recognition with radial basis function (RBF) neural networks. IEEE Transactions on Neural Networks, 13(3), 697–710. https://doi.org/10.1109/TNN.2002.1000134
DOI: https://doi.org/10.1109/TNN.2002.1000134
Google Scholar
Nalband, S., Prince, A., & Agrawal, A. (2018). Entropy‐based feature extraction and classification of vibroarthographic signal using complete ensemble empirical mode decomposition with adaptive noise. IET Science, Measurement & Technology, 12(3), 350–359. https://doi.org/10.1049/iet-smt.2017.0284
DOI: https://doi.org/10.1049/iet-smt.2017.0284
Google Scholar
Nevalainen, M. T., Veikkola, O., Thevenot, J., Tiulpin, A., Hirvasniemi, J., Niinimäki, J., & Saarakkala, S. S. (2021). Acoustic emissions and kinematic instability of the osteoarthritic knee joint: Comparison with radiographic findings. Scientific Reports, 11, 19558. https://doi.org/10.1038/s41598-021-98945-2
DOI: https://doi.org/10.1038/s41598-021-98945-2
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, 929–930. https://doi.org/10.1136/ard.2009.112599
DOI: https://doi.org/10.1136/ard.2009.112599
Google Scholar
Rangayyan, R. M., Oloumi, F., Wu, Y., & Cai, S. (2013). Fractal analysis of knee-joint vibroarthrographic signals via power spectral analysis. Biomedical Signal Processing and Control, 8(1), 23-29. https://doi.org/10.1016/j.bspc.2012.05.004
DOI: https://doi.org/10.1016/j.bspc.2012.05.004
Google Scholar
Riecke, B. F., Christensen, R., Torp-Pedersen, S., Boesen, M., Gudbergsen, H., & Bliddal, H. (2014). An ultrasound score for knee osteoarthritis: A cross-sectional validation study. Osteoarthritis and Cartilage, 22(10), 1675–1691. https://doi.org/10.1016/j.joca.2014.06.020
DOI: https://doi.org/10.1016/j.joca.2014.06.020
Google Scholar
Rogala, M., Gajewski, J., & Ferdynus, M. (2019). Numerical analysis of the thin-walled structure with different trigger locations under axial load. IOP Conference Series: Materials Science and Engineering, 710, 012028. https://doi.org/10.1088/1757-899X/710/1/012028
DOI: https://doi.org/10.1088/1757-899X/710/1/012028
Google Scholar
Rogala, M., Gajewski, J., & Górecki, M. (2021). Study on the effect of geometrical parameters of a hexagonal trigger on energy absorber performance using ANN. Materials, 14(20), 5981. https://doi.org/10.3390/ma14205981
DOI: https://doi.org/10.3390/ma14205981
Google Scholar
Schlüter, D. K., Spain, L., Quan, W., Southworth, H., Platt, N., Mercer, J., Shark, L. K., Waterton, J. C., Bowes, M., Diggle, P. J., Dixon, M., Huddleston, J., & Goodacre, J. (2019). Use of acoustic emission to identify novel candidate biomarkers for knee osteoarthritis (OA). PLOS ONE, 14(10), e0223711. https://doi.org/10.1371/journal.pone.0223711
DOI: https://doi.org/10.1371/journal.pone.0223711
Google Scholar
Shaik, A. B., & Srinivasan, S. (2019). A brief survey on random forest ensembles in classification model. In S. Bhattacharyya, A. E. Hassanien, D. Gupta, A. Khanna & I. Pan (Eds.), International Conference on Innovative Computing and Communications (Vol. 56, pp. 253–260). Springer Singapore. https://doi.org/10.1007/978-981-13-2354-6_27
DOI: https://doi.org/10.1007/978-981-13-2354-6_27
Google Scholar
Shidore, M. M., Athreya, S. S., Deshpande, S., & Jalnekar, R. (2021). Screening of knee-joint vibroarthrographic signals using time and spectral domain features. Biomedical Signal Processing and Control, 68, 102808. https://doi.org/10.1016/j.bspc.2021.102808
DOI: https://doi.org/10.1016/j.bspc.2021.102808
Google Scholar
Singh, J. A., Yu, S., Chen, L., & Cleveland, J. D. (2019). Rates of total joint replacement in the United States: future projections to 2020–2040 using the National Inpatient Sample. The Journal of Rheumatology, 46(9), 1134–1140. https://doi.org/10.3899/jrheum.170990
DOI: https://doi.org/10.3899/jrheum.170990
Google Scholar
Solivetti, F. M., Guerrisi, A., Salducca, N., Desiderio, F., Graceffa, D., Capodieci, G., Romeo, P., Sperduti, I., & Canitano, S. (2016). Appropriateness of knee MRI prescriptions: Clinical, economic and technical issues. La Radiologia Medica, 121, 315-322. https://doi.org/10.1007/s11547-015-0606-1
DOI: https://doi.org/10.1007/s11547-015-0606-1
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
W-Dahl, A., Kärrholm, J., Rogmark, C., Mohaddes, M., Carling, M., Sundberg, M., Bülow, E., Nåtman, J., Carlsen, H., Isaksson, R., & Rolfson, O. (2022). Annual Report 2022. Swedish Arthroplasty Register. https://registercentrum.blob.core.windows.net/refdocs/10.18158/BklrLg8NOo.pdf
Google Scholar
Williams, J., & Pierre-Louis, K. (2024). Osteoarthritis of the Knee. Physician Assistant Clinics, 9(1), 59–69. https://doi.org/10.1016/j.cpha.2023.08.003
DOI: https://doi.org/10.1016/j.cpha.2023.08.003
Google Scholar
Wu, Y., Cai, S., Yang, S., Zheng, F., & Xiang, N. (2013). Classification of knee joint vibration signals using bivariate feature distribution estimation and maximal posterior probability fecision criterion. Entropy, 15(4), 1375-1387. https://doi.org/10.3390/e15041375
DOI: https://doi.org/10.3390/e15041375
Google Scholar
Wu, Z., & Huang, N. E. (2009). Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis, 01(01), 1–41. https://doi.org/10.1142/S1793536909000047
DOI: https://doi.org/10.1142/S1793536909000047
Google Scholar
Yang, S., Cai, S., Zheng, F., Wu, Y., Liu, K., Wu, M., Zou, Q., & Chen, J. (2014). Representation of fluctuation features in pathological knee joint vibroarthrographic signals using kernel density modeling method. Medical Engineering & Physics, 36(10), 1305–1311. https://doi.org/10.1016/j.medengphy.2014.07.008
DOI: https://doi.org/10.1016/j.medengphy.2014.07.008
Google Scholar
Zhang, S., Li, X., Zong, M., Zhu, X., & Cheng, D. (2017). Learning k for kNN classification. ACM Transactions on Intelligent Systems and Technology, 8(3), 1–19. https://doi.org/10.1145/2990508
DOI: https://doi.org/10.1145/2990508
Google Scholar
Zhang, Y. (2012). Support vector machine classification algorithm and its application. In C. Liu, L. Wang, & A. Yang (Eds.), Information Computing and Applications (Vol. 308, pp. 179–186). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-34041-3_27
DOI: https://doi.org/10.1007/978-3-642-34041-3_27
Google Scholar
Authors
Robert KARPIŃSKIr.karpinski@pollub.pl
Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland
https://orcid.org/0000-0003-4063-8503
Authors
Przemysław KRAKOWSKIMedical University of Lublin, Chair and Department of Traumatology and Emergency Medicine, Staszica 11, 20-081 Lublin, Poland, przemyslawkrakowski@umlub.pl, Orthopaedic and Sports Traumatology Department, Carolina Medical Center, Pory 78, 02-757, Warsaw, Poland Poland
Authors
Józef JONAKLublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin, Poland, Poland
Authors
Anna MACHROWSKALublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin, Poland, Poland
Authors
Marcin MACIEJEWSKILublin University of Technology, Faculty of Electrical Engineering and Computer Science, Institute of Electronics and Information Technology, Nadbystrzycka 36, 20-618 Lublin, Poland, Poland
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