Abstract
Knee osteoarthritis (KOA) is a degenerative disease that mainly affects the elderly. The development of this disease is associated with a complex set of factors that cause abnormalities in motor functions. The purpose of this review is to understand the composition of works that combine biomechanical data and machine learning techniques to classify KOA progress. This study was based on research articles found in the search engines Scopus and PubMed between January 2010 and April 2021. The results were divided into data acquisition, feature engineering, and algorithms to synthesize the discovered content. Several approaches have been found for KOA classification with significant accuracy, with an average of 86% overall and three papers reaching 100%; that is, they did not fail once in their tests. The acquisition of data proved to be the divergent task between the works, the most considerable correlation in this stage was the use of the ground reaction force (GRF) sensor. Although three studies reached 100% in the classification, two did not use a gradual evaluation scale, classifying between KOA or healthy individuals. Thus, we can get out of this work that machine learning techniques are promising for identifying KOA using biomechanical data. However, the classification of pathological stages is a complex problem to discuss, mainly due to the difficult access and lack of standardization in data acquisition.
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References
Amer, H.S.A., Sabbahi, M.A., Alrowayeh, H.N., Bryan, W.J., Olson, S.L.: Electromyographic activity of quadriceps muscle during sit-to-stand in patients with unilateral knee osteoarthritis. BMC Res. Notes 11, 356 (2018). https://doi.org/10.1186/s13104-018-3464-9
Bijlsma, J.W., Berenbaum, F., Lafeber, F.P.: Osteoarthritis: an update with relevance for clinical practice. Lancet 377(9783), 2115–2126 (2011). https://doi.org/10.1016/S0140-6736(11)60243-2
Chu, C.R., Williams, A.A., Coyle, C.H., Bowers, M.E.: Early diagnosis to enable early treatment of pre-osteoarthritis. Arthritis Res. Ther. 14, 1–10 (2012). https://doi.org/10.1186/ar3845
Kobsar, D., Osis, S.T., Boyd, J.E., Hettinga, B.A., Ferber, R.: Wearable sensors to predict improvement following an exercise intervention in patients with knee osteoarthritis. J. Neuroeng. Rehabil. 14(1), 1–10 (2017)
Kokkotis, C., Moustakidis, S., Papageorgiou, E., Giakas, G., Tsaopoulos, D.: Machine learning in knee osteoarthritis: a review. Osteoarthr. Cartil. Open 2(3), 100069 (2020). https://doi.org/10.1016/j.ocarto.2020.100069
Kotti, M., Duffell, L., Faisal, A., Mcgregor, A.: The complexity of human walking: a knee osteoarthritis study. PloS One 9, e107325 (2014). https://doi.org/10.1371/journal.pone.0107325
Kotti, M., Duffell, L.D., Faisal, A.A., McGregor, A.H.: Detecting knee osteoarthritis and its discriminating parameters using random forests. Med. Eng. Phys. 43, 19–29 (2017). https://doi.org/10.1016/j.medengphy.2017.02.004
Kour, N., Gupta, S., Arora, S.: A survey of knee osteoarthritis assessment based on gait. Arch. Comput. Methods Eng. 28(2), 345–385 (2020). https://doi.org/10.1007/s11831-019-09379-z
Kwon, S.B., Ku, Y., Lee, M.C., Kim, H.C., et al.: A machine learning-based diagnostic model associated with knee osteoarthritis severity. Sci. Rep. 10(1), 1–8 (2020)
Lespasio, M.J., Piuzzi, N.S., Husni, M.E., Muschler, G.F., Guarino, A., Mont, M.A.: Knee osteoarthritis: a primer. Perm. J. 21, 16–183 (2017). https://doi.org/10.7812/TPP/16-183
Long, M.J., Papi, E., Duffell, L.D., McGregor, A.H.: Predicting knee osteoarthritis risk in injured populations. Clin. Biomech. 47, 87–95 (2017). https://doi.org/10.1016/j.clinbiomech.2017.06.001
McBride, J., et al.: Neural network analysis of gait biomechanical data for classification of knee osteoarthritis. In: Proceedings of the 2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine, pp. 1–4 (2011). https://doi.org/10.1109/BSEC.2011.5872315
Mezghani, N., et al.: Mechanical biomarkers of medial compartment knee osteoarthritis diagnosis and severity grading: discovery phase. J. Biomech. 52, 106–112 (2017). https://doi.org/10.1016/j.jbiomech.2016.12.022
Moustakidis, S., Christodoulou, E., Papageorgiou, E., Kokkotis, C., Papandrianos, N., Tsaopoulos, D.: Application of machine intelligence for osteoarthritis classification: a classical implementation and a quantum perspective. Quantum Mach. Intell. 1(3), 73–86 (2019). https://doi.org/10.1007/s42484-019-00008-3
Moustakidis, S., Theocharis, J., Giakas, G.: A fuzzy decision tree-based SVM classifier for assessing osteoarthritis severity using ground reaction force measurements. Med. Eng. Phys. 32(10), 1145–1160 (2010). https://doi.org/10.1016/j.medengphy.2010.08.006
Muñoz-Organero, M., Littlewood, C., Parker, J., Powell, L., Grindell, C., Mawson, S.: Identification of walking strategies of people with osteoarthritis of the knee using insole pressure sensors. IEEE Sens. J. 17(12), 3909–3920 (2017). https://doi.org/10.1109/JSEN.2017.2696303
Nelson, A.: Osteoarthritis year in review 2017: clinical. Osteoarthr. Cartil. 26(3), 319–325 (2018). https://doi.org/10.1016/j.joca.2017.11.014
Nelson, A.E., Jordan, J.M.: Osteoarthritis: epidemiology and classification. In: Hochberg, M.C., Silman, A.J., Smolen, J.S., Weinblatt, M.E., Weisman, M.H. (eds.) Rheumatology, 6th edn., pp. 1433–1440. Mosby, Philadelphia (2015). https://doi.org/10.1016/B978-0-323-09138-1.00171-6
Phinyomark, A., Osis, S.T., Hettinga, B.A., Kobsar, D., Ferber, R.: Gender differences in gait kinematics for patients with knee osteoarthritis. BMC Musculoskelet. Disord. 17(1), 1–12 (2016)
Vijayvargiya, A., Kumar, R., Dey, N., Tavares, J.M.R.S.: Comparative analysis of machine learning techniques for the classification of knee abnormality. In: 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), pp. 1–6 (2020). https://doi.org/10.1109/ICCCA49541.2020.9250799
Zhang, Y., Jordan, J.M.: Epidemiology of osteoarthritis. Clin. Geriatr. Med. 26(3), 355–369 (2010). https://doi.org/10.1016/j.cger.2010.03.001
Şen Köktaş, N., Yalabik, N., Yavuzer, G., Duin, R.P.: A multi-classifier for grading knee osteoarthritis using gait analysis. Pattern Recogn. Lett. 31(9), 898–904 (2010). https://doi.org/10.1016/j.patrec.2010.01.003
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This work was supported by FCT - Fundação para a Ciência e a Tecnologia under Projects UIDB/05757/2020, UIDB/00319/2020 and individual research grant 2020.05704.BD, funded by Ministério da Ciência, Tecnologia e Ensino Superior (MCTES) and Fundo Social Europeu (FSE) through The Programa Operacional Regional Norte.
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Franco, T., Henriques, P.R., Alves, P., Pereira, M.J.V. (2021). Approaches to Classify Knee Osteoarthritis Using Biomechanical Data. In: Pereira, A.I., et al. Optimization, Learning Algorithms and Applications. OL2A 2021. Communications in Computer and Information Science, vol 1488. Springer, Cham. https://doi.org/10.1007/978-3-030-91885-9_31
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