STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Mechatronics and Automation Original Research

Revolutionizing Knee Osteoarthritis Diagnosis: Unleashing the Potential of Vision Transformers

  1. Suman Rani Computer science, Uttaranchal University
  2. Minakshi Memoria Computer science, Uttaranchal University

Abstract

Osteoarthritis (OA) is the most common kind of arthritis. By analysing data from both sides of the knee joints, radiologists use the Kellgren–Lawrence (KL) grading system to determine the severity of osteoarthritis (OA). The need for knee arthroplasties has increased as a result of this. Recently, there have been proposals for computer-assisted techniques to improve the precision of OA diagnosis. Choosing between conservative and surgical treatment options for knee osteoarthritis (OA) requires an accurate diagnosis and classification of the condition. Consequently, standard weight- bearing knee X-rays are heavily relied upon by many orthopaedic surgeons. Enhancing these tests' consistency and repeatability can have major benefits. Recent developments in artificial intelligence (AI) have shown promise, especially in the area of deep learning. Earlier, the diagnosis of knee osteoarthritis was done using deep learning and DNN. But in the future, in the diagnosis of knees, it will use computer vision techniques. The purpose of this research is to evaluate how well artificial intelligence (AI) can classify the degree of osteoarthritis M(OA) in the knee by taking into account entire image sets and taking common visual anomalies like casts, implants, and non-degenerative diseases into account. In this study, we present an architectural model—based on the ViT transformer—for categorising osteoarthritis in the knee. We will use ViT to classify knee osteoarthritis in the future and gather information from multiple sources. In this study discuss the how ViT used in knee osteoarthritis diagnosis

Keywords

References (23)

  1. Kohn MD, Sassoon AA, Fernando ND. Classifications in Brief: Kellgren-Lawrence Classification of Osteoarthritis. Clinical Orthopaedics & Related Research. 2016;474(8):1886-1893. doi:10.1007/s11999-016-4732-4
  2. Saini, D., Khosla, A., Chand, T., Chouhan, D. K., & Prakash, M. (2023). Automated knee osteoarthritis severity classification using three‐stage preprocessing method and VGG16 architecture. International Journal of Imaging Systems and Technology, 33(3), 1028-1047.
  3. Vaishya R, Pariyo GB, Agarwal AK, Vijay V. Non-operative management of osteoarthritis of the knee joint. Journal of Clinical Orthopaedics and Trauma. 2016;7(3):170-176. doi:10.1016/j.jcot.2016.05.005
  4. Leung K, Zhang B, Tan J, Shen Y, Geras KJ, Babb JS, et al. Prediction of Total Knee Replacement and Diagnosis of Osteoarthritis by Using Deep Learning on Knee Radiographs: Data from the Osteoarthritis Initiative. Radiology. 2020;296(3):584-593. doi:10.1148/radiol.2020192091
  5. B. Schiratti et al., “A deep learning method for predicting knee osteoarthritis radiographic progression from MRI,” Arthritis Res. Ther., vol. 23, no. 1, pp. 1–10, 2021, doi:10.1186/s13075- 021-02634-4.
  6. Guangyi. et al., “Subchondral bone in osteoarthritis: Insight into risk factors and microstructural changes,” Arthritis Res. Ther., vol. 15, p. 223, 2013, [Online]. Available: http://arthritis- research.com/content/15/6/223%5Cnhttp://ovidsp. ovid.com/ovidweb.cgi?T=JS&PAGE=reference& D=emed11&NEWS=N&AN=2013776791
  7. S. Cruz, H. C. Lins, R. V. A. Medeiros, J. M. F. Filho, and S. G. Silva, “Artificial intelligence on the identification of risk groups for osteoporosis, a general review,” Biomed. Eng. Online, vol. 17, no. 1, pp. 1–17, 2018, doi:10.1186/s12938-018-0436- 1.
  8. Kokkotis C, Moustakidis S, Papageorgiou E, Giakas G, Tsaopoulos DE. Machine learning in knee osteoarthritis: A review. Osteoarthritis and Cartilage Open. 2020;2(3):100069. doi:10.1016/j.ocarto.2020.100069
  9. Vos T, Flaxman AD, Naghavi M, Lozano R, Michaud C, Ezzati M, et al. Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet. 2012;380(9859):2163-2196. doi:10.1016/s0140-6736(12)61729-2
  10. Binvignat M, Pedoia V, Butte AJ, Louati K, Klatzmann D, Berenbaum F, et al. Use of machine learning in osteoarthritis research: a systematic literature review. RMD Open. 2022;8(1):e001998. doi:10.1136/rmdopen-2021-001998
  11. Kokkotis C, Moustakidis S, Baltzopoulos V, Giakas G, Tsaopoulos D. Identifying Robust Risk Factors for Knee Osteoarthritis Progression: An Evolutionary Machine Learning Approach. Healthcare. 2021;9(3):260. doi:10.3390/healthcare9030260
  12. S. Gornale, P. U. Patravali, and R. R. Manza, “Detection of Osteoarthritis using Knee X-Ray Image Analyses: A Machine Vision based Approach,” Int. J. Comput. Appl., vol. 145, no. 1, pp. 975–8887, 2016.
  13. El-Ghany SA, Elmogy M, El-Aziz AAA. A fully automatic fine tuned deep learning model for knee osteoarthritis detection and progression analysis. Egyptian Informatics Journal. 2023;24(2):229-240. doi:10.1016/j.eij.2023.03.005
  14. T. Wahyuningrum, A. Lilik, and P. I, “11_ICAwST.2019.8923284.pdf,” 2019 IEEE 10th Int. Conf. Aware. Sci. Technol., pp. 1–6.
  15. Lee LS, Chan PK, Wen C, Fung WC, Cheung A, Chan VWK, et al. Artificial intelligence in diagnosis of knee osteoarthritis and prediction of arthroplasty outcomes: a review. Arthroplasty. 2022;4(1). doi:10.1186/s42836-022-00118-7
  16. Yeoh PSQ, Lai KW, Goh SL, Hasikin K, Hum YC, Tee YK, et al. Emergence of Deep Learning in Knee Osteoarthritis Diagnosis. Computational Intelligence and Neuroscience. 2021;2021(1). doi:10.1155/2021/4931437
  17. Guida C, Zhang M, Shan J. Knee Osteoarthritis Classification Using 3D CNN and MRI. Applied Sciences. 2021;11(11):5196. doi:10.3390/app11115196
  18. Wang Y, Wang X, Gao T, Du L, Liu W. An Automatic Knee Osteoarthritis Diagnosis Method Based on Deep Learning: Data from the Osteoarthritis Initiative. Journal of Healthcare Engineering. 2021;2021:1-10. doi:10.1155/2021/5586529
  19. Dur, “FINAL MASTER THESIS Area of Medicine,” no. January, 2022.
  20. Irshad M, Yasmin M, Sharif MI, Rashid M, Sharif MI, Kadry S. A Novel Light U-Net Model for Left Ventricle Segmentation Using MRI. Mathematics. 2023;11(14):3245. doi:10.3390/math11143245
  21. Ma, C., Chao, Y., Zhu, J., Wang, Y., Liu, W., & Han, Z. (2022, November). Chip Surface Defect Recognition based on Improved Faster R-CNN. In 2022 28th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) (pp. 1-6). IEEE.
  22. Rani S, Memoria M, Choudhury T, Sar A. A Comprehensive Review of Machine Learning’s Role within KOA. EAI Endorsed Transactions on Internet of Things. 2024;10. doi:10.4108/eetiot.5329
  23. Ekram R, Nazer MS. Hospital Admission Profile Due to Osteoarthritis: An Ecological Study. Cureus. 2023. doi:10.7759/cureus.38435
Support