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Approaches to Classify Knee Osteoarthritis Using Biomechanical Data

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Book cover Optimization, Learning Algorithms and Applications (OL2A 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1488))

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

  1. 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

    Article  Google Scholar 

  2. 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

    Article  Google Scholar 

  3. 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

    Article  Google Scholar 

  4. 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)

    Article  Google Scholar 

  5. 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

    Article  Google Scholar 

  6. 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

    Article  Google Scholar 

  7. 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

    Article  Google Scholar 

  8. 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

    Article  MathSciNet  Google Scholar 

  9. 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)

    Article  Google Scholar 

  10. 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

    Article  Google Scholar 

  11. 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

    Article  Google Scholar 

  12. 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

  13. 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

    Article  Google Scholar 

  14. 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

    Article  Google Scholar 

  15. 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

    Article  Google Scholar 

  16. 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

    Article  Google Scholar 

  17. 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

    Article  Google Scholar 

  18. 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

  19. 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)

    Article  Google Scholar 

  20. 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

  21. 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

    Article  Google Scholar 

  22. 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

    Article  Google Scholar 

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Acknowledgment

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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Authors and Affiliations

  1. Research Centre in Digitalization and Intelligent Robotics, Polytechnic Institute of Bragança, Bragança, Portugal

    Tiago Franco, P. Alves & M. J. Varanda Pereira

  2. ALGORITMI Centre, Department of Informatics, University of Minho, Braga, Portugal

    P. R. Henriques

Authors
  1. Tiago Franco
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  2. P. R. Henriques
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  3. P. Alves
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  4. M. J. Varanda Pereira
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Corresponding author

Correspondence to Tiago Franco .

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Editors and Affiliations

  1. Instituto Politécnico de Bragança, Bragança, Portugal

    Ana I. Pereira

  2. Instituto Politécnico de Bragança, Bragança, Portugal

    Florbela P. Fernandes

  3. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Coelho

  4. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Teixeira

  5. Instituto Politécnico de Bragança, Bragança, Portugal

    Maria F. Pacheco

  6. Instituto Politécnico de Bragança, Bragança, Portugal

    Paulo Alves

  7. Instituto Politécnico de Bragança, Bragança, Portugal

    Rui P. Lopes

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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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  • DOI: https://doi.org/10.1007/978-3-030-91885-9_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91884-2

  • Online ISBN: 978-3-030-91885-9

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