Error
  • The authentification system partialy failed, sorry for the inconvenience. Please try again later.
Open Access
Issue
ITM Web Conf.
Volume 79, 2025
International Conference on Knowledge Engineering and Information Systems (KEIS-2025)
Article Number 01026
Number of page(s) 8
DOI https://doi.org/10.1051/itmconf/20257901026
Published online 08 October 2025
  1. A. Tiulpin, S. Klein, S.M.A. Bierma-Zeinstra, J. Thevenot, E. Rahtu, J. van Meurs, E.H.G. Oei, S. Saarakkala, Multimodal machine learning-based knee osteoarthritis progression prediction from plain radiographs and clinical data. Sci. Rep. 9, 20038 (2019). https://doi.org/10.1038/s41598-019-56527-3 [Google Scholar]
  2. C.S. Azad, A.K. Singh, P. Pandey, M. Singh, P. Chaudhary, N. Tia, A. Rastogi, Osteoarthritis in India: an epidemiologic aspect. Int. J. Recent Sci. Res. 8, 20918–20922 (2017) [Google Scholar]
  3. P.P. Kawathekar, K.J. Karande, Severity analysis of osteoarthritis of knee joint from X-ray images: A literature review, In Proceedings of the International Conference, Signal Propagation and Computer Technology (ICSPCT 2014), IEEE, Ajmer, India, August 28 (2014), 648–652 [Google Scholar]
  4. A. Jamshidi, J.-P. Pelletier, J. Martel-Pelletier, Machine-learning-based patient-specific prediction models for knee osteoarthritis. Nat. Rev. Rheumatol. 15, 49–60 (2019). https://doi.org/10.1038/s41584-018-0130-5 [Google Scholar]
  5. A. Raza, T.-L. Phan, H.-C. Li, N.V. Hieu, T.T. Nghia, C.T.S. Ching, A comparative study of machine learning classifiers for enhancing knee osteoarthritis diagnosis. Information 15, 183 (2024). https://doi.org/10.3390/info15040183 [Google Scholar]
  6. Z. Zhao, M. Zhao, T. Yang, J. Li, C. Qin, B. Wang, L. Wang, B. Li, J. Liu, Identifying significant structural factors associated with knee pain severity in patients with osteoarthritis using machine learning. Sci. Rep. 14, 14705 (2024). https://doi.org/10.1038/s41598-024-65613-0 [Google Scholar]
  7. T. Tariq, Z. Suhail, Z. Nawaz, Machine learning approaches for the classification of knee osteoarthritis, In Proc. 3rd Int. Conf. Electrical, Computer, Communications and Mechatronics Engineering (ICECCME 2023), IEEE, Tenerife, Canary Islands, Spain, July 19–21 (2023), 1–6 [Google Scholar]
  8. H.J. Yoo, H.W. Jeong, S.B. Park, S.J. Shim, H.S. Nam, Y.S. Lee, Do individualized patient-specific situations predict the progression rate and fate of knee osteoarthritis? Prediction of knee osteoarthritis. J. Clin. Med. 12, 1204 (2023). https://doi.org/10.3390/jcm12031204 [Google Scholar]
  9. S.S. Gornale, P.U. Patravali, P.S. Hiremath, Automatic detection and classification of knee osteoarthritis using Hu’s invariant moments, Front. Robot. AI 7, 591827 (2020). [Google Scholar]
  10. C. Kokkotis, C. Ntakolia, S. Moustakidis, G. Giakas, D. Tsaopoulos, Explainable machine learning for knee osteoarthritis diagnosis based on a novel fuzzy feature selection methodology. Phys. Eng. Sci. Med. 45, 219–229 (2022). https://doi.org/10.1007/s13246-022-01106-6 [Google Scholar]
  11. C. Ntakolia, C. Kokkotis, S. Moustakidis, D. Tsaopoulos, Prediction of joint space narrowing progression in knee osteoarthritis patients. Diagnostics 11, 285 (2021). https://doi.org/10.3390/diagnostics11020285 [Google Scholar]
  12. R. Mahum, S.U. Rehman, T. Meraj, H.T. Rauf, A. Irtaza, A.M. El-Sherbeeny, M.A. El-Meligy, A novel hybrid approach based on deep CNN features to detect knee osteoarthritis. Sensors 21, 6189 (2021). https://doi.org/10.3390/s21186189 [CrossRef] [PubMed] [Google Scholar]
  13. Li, Hong-bo, Yong-jun Du, Guy Romeo Kenmegne, and Cheng-wei Kang, Machine learning analysis of serum cholesterol's impact on knee osteoarthritis progression, Scientific Reports 14, 18852. (2024) [Google Scholar]
  14. R.T. Wahyuningrum, L. Anifah, I.K.E. Purnama, M.H. Purnomo, A new approach to classify knee osteoarthritis severity from radiographic images based on CNN-LSTM method in Proceedings of the 10th IEEE International Conference, Awareness Science and Technology (iCAST 2019), IEEE, Morioka, Japan, December 05 (2019), 1–6 [Google Scholar]
  15. E. Christodoulou, S. Moustakidis, N. Papandrianos, D. Tsaopoulos, E. Papageorgiou, Exploring deep learning capabilities in knee osteoarthritis case study for classification, In Proceedings of the 10th IEEE International Conference, Information, Intelligence, Systems and Applications (IISA 2019), IEEE, Patras, Greece, November 14 (2019), 1–6 [Google Scholar]
  16. S. Ben Hassine, A. Balti, S. Abid, M.M. Ben Khelifa, M. Sayadi, Markerless vision-based knee osteoarthritis classification using machine learning and gait videos. Front. Signal Process. 4, 1479244 (2024). https://doi.org/10.3389/frsip.2024.1479244 [Google Scholar]
  17. S.M. Ahmed, R.J. Mstafa, Identifying severity grading of knee osteoarthritis from X-ray images using an efficient mixture of deep learning and machine learning models. Diagnostics 12, 2939 (2022). https://doi.org/10.3390/diagnostics12122939 [Google Scholar]
  18. A.I. Ahmad, N. Khac-Lan, T. Hechmi, J. Rachid, L. Eric, Prediction of knee osteoarthritis progression using radiological descriptors obtained from bone texture analysis and Siamese neural networks: data from OAI and MOST cohorts. Arthritis Res. Ther. 24, 1–13 (2022). [Google Scholar]
  19. W. Chen, H. Zheng, B. Ye, T. Guo, Y. Xu, Z. Fu, Q. Deng, Identification of biomarkers for knee osteoarthritis through clinical data and machine learning models. Sci. Rep. 15, 1703 (2025). https://doi.org/10.1038/s41598-025-85945-9 [Google Scholar]
  20. L. Shen, S. Yue, A clinical model to predict the progression of knee osteoarthritis: data from Dryad, J. Orthop. Surg. Res. 18, 628 (2023). https://doi.org/10.1186/s13018-023-04118-4 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.