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Detection of Knee Osteoarthritis and Prediction of Its Severity Using X-ray Image Analysis and Patients Assessment Data: A Hybrid Design

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6th Kuala Lumpur International Conference on Biomedical Engineering 2021 (BIOMED 2021)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 86 ))

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Abstract

Knee osteoarthritis (KOA) is a degenerative disease associated with cartilage loss, causes limitations in the range of movement, and known to be one of the most disabling age-associated diseases around the world. It is vital to predict its presence and severity at early stage to tailor the interventions and treatments properly. Traditionally, X-ray Images are graded by radiologists to quantify KOA severity; however, this approach suffers from high levels of subjectivity due to the semi-quantitative nature of grading systems. Numerous attempts have been made to recruit automated X-ray image analysis to quantify KOA severity, but few studies have used pertinent assessment data such as symptoms and medications being used to establish accurate predictive model. So, we proposed a statistical model built on combination of features extracted from X-ray images and patients’ data using ordinal regression analysis. The results revealed that the developed model based on combination of KOA X-ray key features and patient assessment data is able to predict the severity of KOA with high level of accuracy (89.2%) and acceptable level of inter-rater reliability with quadratic weighted Cohen’s Kappa coefficient (QWK) of 0.8337. The study outcomes suggested that variables showing impaired knee functions are the best indicators to quantify knee OA presence and severity that may be used in conjunction with X-ray biomarkers for develo** intervention and targeted treatment.

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References

  1. James, S.L., Abate, D., Abate, K.H., Abay, S.M., Abbafati, C., Abbasi, N., …, Abdelalim, A.: Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the global burden of disease study 2017. The Lancet 392(10159), 1789-1858 (2018)

    Google Scholar 

  2. Abedin, J., Antony, J., McGuinness, K., Moran, K., O’Connor, N.E., Rebholz-Schuhmann, D., Newell, J.: Predicting knee osteoarthritis severity: comparative modeling based on patient’s data and plain X-ray images. Sci. Rep. 9(1), 1–11 (2019)

    Article  Google Scholar 

  3. Heidari, B.: Knee osteoarthritis prevalence, risk factors, pathogenesis and features: part I. Caspian J. Intern. Med. 2(2), 205 (2011)

    Google Scholar 

  4. Clausen, B., Holsgaard-Larsen, A., Søndergaard, J., Christensen, R., Andriacchi, T.P., Roos, E.M.: The effect on knee-joint load of instruction in analgesic use compared with neuromuscular exercise in patients with knee osteoarthritis: study protocol for a randomized, single-blind, controlled trial (the EXERPHARMA trial). Trials 15(1), 444 (2014). https://doi.org/10.1186/1745-6215-15-444

  5. Kellgren, J., Lawrence, J.: Radiological assessment of osteo-arthrosis. Ann. Rheum. Dis. 16(4), 494 (1957)

    Article  Google Scholar 

  6. Abdelaziz, H., Balde, O.M., Citak, M., Gehrke, T., Magan, A., Haasper, C.: Kellgren-Lawrence scoring system underestimates cartilage damage when indicating TKA: preoperative radiograph versus intraoperative photograph. Arch. Orthop. Trauma Surg. 139(9), 1287–1292 (2019)

    Article  Google Scholar 

  7. Saleem, M., Farid, M.S., Saleem, S., Khan, M.H.: X-ray image analysis for automated knee osteoarthritis detection. SIViP 14(6), 1079–1087 (2020). https://doi.org/10.1007/s11760-020-01645-z

    Article  Google Scholar 

  8. Pandey, M.S., Rajitha, B., Agarwal, S.: Computer assisted automated detection of knee osteoarthritis using X-ray images. Sci. Technol. 1(2), 74–79 (2015)

    Google Scholar 

  9. Stachowiak, G.W., Wolski, M., Woloszynski, T., Podsiadlo, P.: Detection and prediction of osteoarthritis in knee and hand joints based on the X-ray image analysis. Biosurf. Biotribol. 2(4), 162–172 (2016)

    Article  Google Scholar 

  10. Hunter, D.J., McDougall, J.J., Keefe, F.J.: The symptoms of osteoarthritis and the genesis of pain. Rheum. Dis. Clin. North Am. 34(3), 623–643 (2008). https://doi.org/10.1016/j.rdc.2008.05.004

    Article  Google Scholar 

  11. Altman, R., Alarcón, G., Appelrouth, D., Bloch, D., Borenstein, D., Brandt, K., …, Wolfe, F.: The American college of rheumatology criteria for the classification and reporting of osteoarthritis of the hip. Arthritis Rheum. 34(5), 505–514 (1991).https://doi.org/10.1002/art.1780340502

  12. Cicuttini, F., Ding, C., Wluka, A., Davis, S., Ebeling, P.R., Jones, G.: Association of cartilage defects with loss of knee cartilage in healthy, middle-age adults: a prospective study. Arthritis Rheum. 52(7), 2033–2039 (2005). https://doi.org/10.1002/art.21148

    Article  Google Scholar 

  13. Mengko, T.L., Wachjudi, R.G., Suksmono, A.B., Danudirdjo, D.: Automated detection of unimpaired joint space for knee osteoarthritis assessment. Paper presented at the Proceedings of 7th International Workshop on Enterprise networking and Computing in Healthcare Industry, 23–25 June 2005. HEALTHCOM (2005)

    Google Scholar 

  14. Schmidt, J.E., Amrami, K.K., Manduca, A., Kaufman, K.R.: Semi-automated digital image analysis of joint space width in knee radiographs. Skeletal Radiol. 34(10), 639–643 (2005). https://doi.org/10.1007/s00256-005-0908-9

    Article  Google Scholar 

  15. Jones, T.D., Plassmann, P. An active contour model for measuring the area of leg ulcers. IEEE Trans. Med. Imaging 19(12), 1202–1210 (2000). https://doi.org/10.1109/42.897812

  16. AmigĂł, E., Gonzalo, J., Mizzaro, S., Carrillo-de-Albornoz, J.: An effectiveness metric for ordinal classification: formal properties and experimental results (2020). https://doi.org/10.18653/v1/2020.acl-main.363

  17. Yong, C.W., Teo, K., Murphy, B.P., Hum, Y.C., Tee, Y.K., **a, K., Lai, K.W.: Knee osteoarthritis severity classification with ordinal regression module. Multi. Tools Appl. (2021). https://doi.org/10.1007/s11042-021-10557-0

  18. Fayers, P.M., Machin, D.: Quality of Life: The Assessment, Analysis and Interpretation of Patient-Reported Outcomes. John Wiley & Sons (2013)

    Google Scholar 

  19. Antony, J., McGuinness, K., O’Connor, N.E., Moran, K.: Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 1195–1200. IEEE (2016)

    Google Scholar 

  20. Tiulpin, A., Tevenot, J., Rahtu, E., Lehenkari, P., Saarakkala, S.: Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach. Sci. reports 8, 1727 (2018)

    Google Scholar 

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Authors and Affiliations

  1. Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Jalan Universiti, 50603, Kuala Lumpur, Malaysia

    Hamidreza Mohafez, Hamza Sayed & Lai Khin Wee

  2. Centre for Sport and Exercise Sciences, Universiti Malaya, Jalan Universiti, 50603, Kuala Lumpur, Malaysia

    Maryam Hadizadeh

  3. Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Selangor, Malaysia

    Siti Anom Ahmad

Authors
  1. Hamidreza Mohafez
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  2. Hamza Sayed
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  3. Maryam Hadizadeh
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  4. Lai Khin Wee
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  5. Siti Anom Ahmad
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Correspondence to Hamidreza Mohafez .

Editor information

Editors and Affiliations

  1. Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia

    Juliana Usman

  2. Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia

    Yih Miin Liew

  3. Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia

    Mohd Yazed Ahmad

  4. Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia

    Fatimah Ibrahim

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Mohafez, H., Sayed, H., Hadizadeh, M., Wee, L.K., Ahmad, S.A. (2022). Detection of Knee Osteoarthritis and Prediction of Its Severity Using X-ray Image Analysis and Patients Assessment Data: A Hybrid Design. In: Usman, J., Liew, Y.M., Ahmad, M.Y., Ibrahim, F. (eds) 6th Kuala Lumpur International Conference on Biomedical Engineering 2021. BIOMED 2021. IFMBE Proceedings, vol 86 . Springer, Cham. https://doi.org/10.1007/978-3-030-90724-2_16

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  • DOI: https://doi.org/10.1007/978-3-030-90724-2_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90723-5

  • Online ISBN: 978-3-030-90724-2

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