A Survey on Computer Aided Methods for Diagnosis and Assessment of Knee Osteoarthritis

Authors

  • D. Pavithra  PG Scholar, Department of Biomedical Instrumentation Engineering, School of Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India

Keywords:

Knee Osteoarthritis, Classification, Computer Aided Diagnosis, Grading of OA, Neural Networks

Abstract

Knee Osteoarthritis (OA) is the most common joint disorder that mainly occurs due to wear down of cartilage. An early diagnosis has a pivotal role in treating osteoarthritis and in attenuating further effects. The analysis of medical images is done manually by the medical expert, which is time consuming, subjective and sometimes unpredictable. The complexities related to the medical images make it hard to examine them in an effective way. Thus, to overcome these difficulties several computer-aided methods are being adopted. This paper provides study and analysis of recently developed computer aided methods for diagnosis of knee osteoarthritis and assessment of its severity.

References

  1. www.practicalpainmanagement.com
  2. M. D. Kohn, A. A. Sassoon, and N. D. Fernando, “Classifications in Brief: Kellgren-Lawrence Classification of Osteoarthritis,” Clin. Orthop. Relat. Res., vol. 474, no. 8, pp. 1886–1893, 2016.
  3. Q. Li, K. Amano, T. Link and C. Ma, "Advanced Imaging in Osteoarthritis", Sports Health: A Multidisciplinary Approach, vol. 8, no. 5, pp. 418-428, 2016. Available: 10.1177/1941738116663922.
  4. B. Ashinsky et al., "Predicting early symptomatic osteoarthritis in the human knee using machine learning classification of magnetic resonance images from the osteoarthritis initiative", Journal of Orthopaedic Research, vol. 35, no. 10, pp. 2243- 2250, 2017. Available: 10.1002/jor.23519
  5. V. Kumar and A. Jayanthy, "Classification of MRI images in 2D coronal view and measurement of articular cartilage thickness for early detection of knee osteoarthritis", 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), 2016. Available: 10.1109/rteict.2016.7808167.
  6. U. Aprilliani and Z. Rustam, "Osteoarthritis Disease Prediction Based on Random Forest", 2018 mInternational Conference on Advanced Computer Science and Information Systems (ICACSIS), 2018. Available: 10.1109/icacsis.2018.8618166.
  7. Pierre Dodin, Jean-Pierre Pelletier, Johanne Martel Pelletier, and François Abram, “Automatic human knee cartilage segmentation from 3D magnetic resonance images”, IEEE Trans. on Biomedical Engineering, vol. 57, no. 11, pp. 2699- 2711, 2010.
  8. Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., & Kijowski, R. (2017). “Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging”, Magnetic Resonance in Medicine, 79(4), 2379–2391. doi:10.1002/mrm.26841
  9. Raj, A., Vishwanathan, S., Ajani, B., Krishnan, K., & Agarwal,
  10. H. (2018). “Automatic knee cartilage segmentation using fully volumetric convolutional neural networks for evaluation of osteoarthritis”, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). doi:10.1109/isbi.2018.8363705
  11. J. Mu, X. Liu, S. Luan, P. Heintz, G. Mlady and D. Chen, "Segmentation of knee joints in x-ray images using decomposition-based sweeping and graph search", Medical Imaging 2011: Image Processing, 2011. Available: 10.1117/12.878414
  12. Neogi, T., Felson, D., Niu, J., Nevitt, M., Lewis, C. E., Aliabadi, Zhang, Y. Association between radiographic features of knee osteoarthritis and pain: results from two cohort studies. BMJ, 339(aug21 1), b2844–b2844, 2009. Available: doi:10.1136/bmj.b2844
  13. R. Hegadi, D. Navale, T. Pawar and D. Ruikar, "Osteoarthritis Detection and Classification from Knee X-Ray Images Based on Artificial Neural Network", Communications in Computer and Information Science, pp. 97-105, 2019. Available: 10.1007/978-981-13-9184-2_8.
  14. Yoo TK, Kim DW, Choi SB, Oh E, Park JS,” Simple Scoring System and Artificial Neural Network for Knee Osteoarthritis Risk Prediction: A Cross-Sectional Study”. PLoS ONE 11(2): e0148724, 2016.
  15. G. Vijayakumari, and Ganga Holi. "Assessment Of Joint Space In Knee Osteoarthritis Using Particle Swarm Optimization Technique". International Journal Of Innovative Technology And Exploring Engineering, vol 8, no. 12, 2019, pp. 5818- 5823. Blue Eyes Intelligence Engineering And Sciences Engineering And Sciences Publication – BEIESP.
  16. Aamir Yousuf Bhat, A. Suhasini, “Normal And Abnormal Detection For Knee Osteoarthritis Using Machine Learning Techniques”, International Journal of Recent Technology and Engineering, 8(2), pp.6026-6033, 2019.
  17. A. Brahim et al., "A decision support tool for early detection of knee OsteoArthritis using X-ray imaging and machine learning: Data from the OsteoArthritis Initiative", Computerized Medical Imaging and Graphics, vol. 73, pp. 11-18, 2019. Available: 10.1016/j.compmedimag.2019.01.007.
  18. P. Chen, L. Gao, X. Shi, K. Allen and L. Yang, "Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss", Computerized Medical Imaging and Graphics, vol. 75, pp. 84-92, 2019. Available: 10.1016/j.compmedimag.2019.06.002.
  19. Lilik Anifah, Ketut Eddy Purnama, Mochamad Hariadi, Mauridhi Hery Purnomo, “Osteoarthritis Classification Using Self Organizing Map Based on Gabor Kernel and Contrast- Limited Adaptive Histogram Equalization”, The Open
  20. Biomedical Engineering Journal, 7(1), 18–28. 2013. Available: doi:10.2174/1874120701307010018.
  21. Wahyuningrum, R. T., Anifah, L., Purnama, I. K. E., & Purnomo, M. H. (2016). “A novel hybrid of S2DPCA and SVM for knee osteoarthritis classification”, IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), 2016. Available: doi:10.1109/civemsa.2016.7524317.
  22. Mahima Shanker Pandey, Rajitha B, Suneeta Agarwal, “Computer Assisted Automated Detection of Knee Osteoarthritis using X-ray Images”, Science & Technology, 2015, 1(2), 74-79.
  23. A. Tiulpin, J. Thevenot, E. Rahtu, P. Lehenkari and S. Saarakkala, "Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach", Scientific Reports, vol. 8, no. 1, 2018. Available: 10.1038/s41598-018-20132-7.
  24. R. Wahyuningrum, L. Anifah, I. Eddy Purnama and M. Hery Purnomo, "A New Approach to Classify Knee Osteoarthritis Severity from Radiographic Images based on CNN-LSTM Method", 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST), 2019. Available: 10.1109/icawst.2019.8923284
  25. A. Kumar, T. Blessia, S. Singh and J. Vennila, "Application of Knowledge Based System for Diagnosis of Osteoarthritis", Journal of Artificial Intelligence, vol. 4, no. 4, pp. 269-278, 2011. Available: 10.3923/jai.2011.269.278.
  26. J. Lim, J. Kim and S. Cheon, "A Deep Neural Network-Based Method for Early Detection of Osteoarthritis Using Statistical Data", International Journal of Environmental Research and Public Health, vol. 16, no. 7, p. 1281, 2019. Available: 10.3390/ijerph16071281.
  27. Zhang, W. et al. Nottingham knee osteoarthritis risk prediction models. Ann. Rheum. Dis. 70, 1599–1604 (2011).
  28. E. Losina, K. Klara, G. Michl, J. Collins and J. Katz, "Development and feasibility of a personalized, interactive risk calculator for knee osteoarthritis", BMC Musculoskeletal Disorders, vol. 16, no. 1, 2015. Available: 10.1186/s12891- 015-0771-3.

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Published

2020-03-05

Issue

Section

Research Articles

How to Cite

[1]
D. Pavithra, " A Survey on Computer Aided Methods for Diagnosis and Assessment of Knee Osteoarthritis, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 5, Issue 5, pp.236-242, March-April-2020.