How Can Artificial Intelligence Identify Knee Osteoarthritis from Radiographic Images with Satisfactory Accuracy?: A Literature Review for 2018–2024
Source abstract
Abstract
Knee osteoarthritis is a chronic, progressive disease that rapidly progresses to severe stages. Reliable and accurate diagnosis, combined with the implementation of preventive lifestyle modifications before irreversible damage occurs, can effectively protect patients from becoming an inactive population. Artificial intelligence continues to play a pivotal role in computer-aided diagnosis with increasingly convincing accuracy, particularly in identifying the severity of knee osteoarthritis according to the Kellgren–Lawrence (KL) grading scale. The primary objective of this literature review is twofold. Firstly, it aims to provide a systematic analysis of the current literature on the main artificial intelligence models used recently to predict the severity of knee osteoarthritis from radiographic images. Secondly, it constitutes a critical review of the different methodologies employed and the key elements that have improved diagnostic performance. Ultimately, this study demonstrates that the considerable success of artificial intelligence systems will reinforce healthcare professionals’ confidence in the reliability of machine learning algorithms, facilitating more effective and faster treatment for patients afflicted with knee osteoarthritis. In order to achieve these objectives, a qualitative and quantitative analysis was conducted on 60 original research articles published between 1 January 2018 and 15 May 2024.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Medical Imaging and Analysis · Osteoarthritis Treatment and Mechanisms · Infrared Thermography in Medicine
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
ADOS Team (Equipe Aide à la Décision et Optimisation des Systèmes), LISTD Laboratory, Ecole Nationale Supérieure des Mines de Rabat, Rabat 10000, Morocco
Ministry of Health and Social Protection, Provincial Ministerial Administration of El Kelaa des Sraghna, El Kelaa des Sraghna 43000, Morocco
Multidisciplinary Research Laboratory for Science, Technology and Society, Department of Computer Engineering and Mathematics, Higher School of Technology, Khenifra, Sultan Moulay Slimane University, Beni Mellal 23000, Morocco
Institut Supérieur d’Ingénierie et Technologies de Santé, Faculté de Médecine Abulcasis des Sciences de la Santé, Rabat 10000, Morocco
References (61)
Show source-provided references
1. Learning From Highly Confident Samples for Automatic Knee Osteoarthritis Severity Assessment: Data From the Osteoarthritis Initiative10.1109/JBHI.2021.3102090 ↗
2. VGG16 Based Knee Osteoarthritis Grading Using X-ray Images10.22214/ijraset.2022.46994 ↗
3. Wahyuningrum, R.T., Anifah, L., Eddy Purnama, I.K., and Hery Purnomo, M. (2019, January 23–25). A New Approach to Classify Knee Osteoarthritis Severity from Radiographic Images based on CNN-LSTM Method. Proceedings of the IEEE 10th International Conference on Awareness Science and Technology (ICAST), Morioka, Japan. Available online: https://ieeexplore.ieee.org/document/8923284.10.1109/ICAwST.2019.8923284 ↗
4. A novel method for determining the Femoral-Tibial Angle of Knee Osteoarthritis on X-ray radiographs: Data from the Osteoarthritis Initiative10.1016/j.heliyon.2020.e04433 ↗
5. (2023, August 10). Statistics Adopted from the National Center for Chronic Disease Prevention and Health Promotion, Available online: https://archive.cdc.gov/#/details?q=https://www.cdc.gov/arthritis/data_statistics&start=0&rows=10&url=https://www.cdc.gov/media/releases/2017/p0307-arthritis-climbing.html.
6. Gornale, S.S., Patravali, P.U., and Hiremath, P.S. (2020). A Comprehensive Digital Knee X-ray Image Dataset for the Assessment of Osteoarthritis. JSM Biomed. Imaging Data Pap., 6, Available online: https://www.academia.edu/79637490/A_Comprehensive_Digital_Knee_X_ray_Image_Dataset_for_the_Assessment_of_Osteoarthritis.
7. Machine Learning-Based Automatic Classification of Knee Osteoarthritis Severity Using Gait Data and Radiographic Images10.1109/ACCESS.2020.3006335 ↗
8. Olsson, S., Akbarian, E., Lind, A., Razavian, A.S., and Gordon, M. (2021). Automating classification of osteoarthritis according to Kellgren-Lawrence in the knee using deep learning in an unfiltered adult population. BMC Musculoskelet. Disord., 22.10.1186/s12891-021-04722-7 ↗
9. Can a Convolutional Neural Network Classify Knee Osteoarthritis on Plain Radiographs as Accurately as Fellowship-Trained Knee Arthroplasty Surgeons?10.1016/j.arth.2020.04.059 ↗
10. Tiulpin, A., Melekhov, I., and Saarakkala, S. (2019, January 27–28). KNEEL: Knee Anatomical Landmark Localization Using Hourglass Networks. Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Republic of Korea. Available online: https://ieeexplore.ieee.org/document/9022083.10.1109/ICCVW.2019.00046 ↗
11. Automatic assessment of knee osteoarthritis severity in portable devices based on deep learning10.1186/s13018-022-03429-2 ↗
12. A decision support tool for early detection of knee OsteoArthritis using X-ray imaging and machine learning: Data from the OsteoArthritis Initiative10.1016/j.compmedimag.2019.01.007 ↗
13. Semixup: In- and Out-of-Manifold Regularization for Deep Semi-Supervised Knee Osteoarthritis Severity Grading From Plain Radiographs10.1109/TMI.2020.3017007 ↗
14. Tiulpin, A., and Saarakkala, S. (2020). Automatic Grading of Individual Knee Osteoarthritis Features in Plain Radiographs using Deep Convolutional Neural Networks. Diagnostics, 10.10.3390/diagnostics10110932 ↗
15. Adaptive segmentation of knee radiographs for selecting the optimal ROI in texture analysis10.1016/j.joca.2020.03.006 ↗
16. Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss10.1016/j.compmedimag.2019.06.002 ↗
17. Yunus, U., Amin, J., Sharif, M., Yasmin, M., Kadry, S., and Krishnamoorthy, S. (2022). Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network. Life, 12.10.3390/life12081126 ↗
18. Dalia, Y., Bharath, A., Mayya, V., and Sowmya Kamath, S. (2021, January 24–25). DeepOA: Clinical Decision Support System for Early Detection and Severity Grading of Knee Osteoarthritis. Proceedings of the IEEE 5th International Conference on Computer, Communication and Signal Processing (ICCCSP), Chennai, India. Available online: https://ieeexplore.ieee.org/document/9465522.10.1109/ICCCSP52374.2021.9465522 ↗
19. Pandian, D., Fernando, X., Baig, Z., and Shi, F. A Study on Comparative Analysis of Automated and Semiautomated Segmentation Techniques on Knee Osteoarthritis X-ray Radiographs. Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB), Lecture Notes in Computational Vision and Biomechanics.
20. Nguyen Huu, P., Nguyen Thanh, D., le Thi Hai, T., Chu Duc, H., Pham Viet, H., and Nguyen Trong, C. (2022, January 11–12). Detection and Classification Knee Osteoarthritis Algorithm using YOLOv3 and VGG16 Models. Proceedings of the IEEE 7th National Scientific Conference on Applying New Technology in Green Buildings (ATiGB), Da Nang, Vietnam. Available online: https://ieeexplore.ieee.org/document/9984096.10.1109/ATiGB56486.2022.9984096 ↗
21. A ResNet-based approach for accurate radiographic diagnosis of knee osteoarthritis10.1049/cit2.12079 ↗
22. Deep Neural Network-based Knee Osteoarthritis Grading Using X-rays10.22214/ijraset.2022.41757 ↗
23. Zhang, B., Tan, J., Cho, K., Chang, G., and Deniz, C.M. (2020, January 3–7). Attention-based CNN for KL Grade Classification: Data from the Osteoarthritis Initiative. Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA. Available online: https://ieeexplore.ieee.org/document/9098456.10.1109/ISBI45749.2020.9098456 ↗
24. Yoon, J.S., Yon, C.-J., Lee, D., Lee, J.J., Kang, C.H., Kang, S.-B., Lee, N.-K., and Chang, C.B. (2023). Assessment of a novel deep learning-based software developed for automatic feature extraction and grading of radiographic knee osteoarthritis. BMC Musculoskelet Disord., 24.10.1186/s12891-023-06951-4 ↗
25. Tian, X., Han, C., Wang, J., Tan, Y., Zhu, G., Lei, M., Ma, S., Hu, Y., Li, S., and Chen, H. (2020). Distal tibial tuberosity high tibial osteotomy using an image enhancement technique for orthopedic scans in the treatment of medial compartment knee osteoarthritis. Comput. Methods Programs Biomed., 191.10.1016/j.cmpb.2020.105349 ↗
26. Applying Densely Connected Convolutional Neural Networks for Staging Osteoarthritis Severity from Plain Radiographs10.1007/s10278-018-0098-3 ↗
27. Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data10.1038/s41598-019-56527-3 ↗
28. Knee Osteoarthritis Detection and Classification Using X-rays10.1109/ACCESS.2023.3276810 ↗
29. Abdo, A.A., El-Tarhouni, W., Abdulsalam, A.F., and Altajori, A.B. (2022, January 4–6). Estimating the severity of knee osteoarthritis using Deep Convolutional Neural Network based on Contrast Limited Adaptive Histogram Equalization technique. Proceedings of the 2022 International Conference on Engineering & MIS (ICEMIS), Istanbul, Turkey. Available online: https://ieeexplore.ieee.org/document/9914285.10.1109/ICEMIS56295.2022.9914285 ↗
30. Ahmed, S.M., and Mstafa, R.J. (2022). Identifying Severity Grading of Knee Osteoarthritis from X-ray Images Using an Efficient Mixture of Deep Learning and Machine Learning Models. Diagnostics, 12.10.3390/diagnostics12122939 ↗
31. Texture analysis using complex wavelet decomposition for knee osteoarthritis detection: Data from the osteoarthritis initiative10.1016/j.compeleceng.2018.04.004 ↗
32. Mohammed, A.S., Hasanaath, A.A., Latif, G., and Bashar, A. (2023). Knee Osteoarthritis Detection and Severity Classification Using Residual Neural Networks on Preprocessed X-ray Images. Diagnostics, 13.10.3390/diagnostics13081380 ↗
33. Al-Rimy, B.A.S., Saeed, F., Al-Sarem, M., Albarrak, A.M., and Qasem, S.N. (2023). An Adaptive Early Stopping Technique for DenseNet169-Based Knee Osteoarthritis Detection Model. Diagnostics, 13.10.3390/diagnostics13111903 ↗
34. A fully automatic fine-tuned deep learning model for knee osteoarthritis detection and progression analysis10.1016/j.eij.2023.03.005 ↗
35. Tri Wahyuningrum, R., Yasid, A., and Jacob Verkerke, G. (2020, January 25–27). Deep Neural Networks for Automatic Classification of Knee Osteoarthritis Severity Based on X-ray Images. Proceedings of the 8th International Conference on Information Technology ICIT 2020: IoT and Smart City, , Xi’an, China. Available online: https://dl.acm.org/doi/10.1145/3446999.3447020.10.1145/3446999.3447020 ↗
36. Knee Osteoarthritis Severity Prediction using an Attentive Multi-Scale Deep Convolutional Neural Network10.1007/s11042-023-15484-w ↗
37. Sivakumari, T., and Vani, R. (2022, January 22–24). Implementation of AlexNet for Classification of Knee Osteoarthritis. Proceedings of the 2022 7th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India. Available online: https://ieeexplore.ieee.org/document/9835835.10.1109/ICCES54183.2022.9835835 ↗
38. Automated Classification of Radiographic Knee Osteoarthritis Severity Using Deep Neural Networks10.1148/ryai.2020190065 ↗
39. Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach10.1038/s41598-018-20132-7 ↗
40. Predicting knee osteoarthritis severity: Comparative modeling based on patient’s data and plain X-ray images10.1038/s41598-019-42215-9 ↗
41. Automated detection of patellofemoral osteoarthritis from knee lateral view radiographs using deep learning: Data from the Multicenter Osteoarthritis Study (MOST)10.1016/j.joca.2021.06.011 ↗
42. Alshamrani, H.A., Rashid, M., Alshamrani, S.S., and Alshehri, A.H. (2023). Osteo-NeT: An Automated System for Predicting Knee Osteoarthritis from X-ray Images Using Transfer-Learning-Based Neural Networks Approach. Healthcare, 11.10.3390/healthcare11091206 ↗
43. Gornale, S.S., Patravali, P.U., and Hiremath, P.S. (2020). Automatic Detection and Classification of Knee Osteoarthritis Using Hu’s Invariant Moments. Front. Robot. AI, 7.10.3389/frobt.2020.591827 ↗
44. Cueva, J.H., Castillo, D., Espinós-Morató, H., Durán, D., Díaz, P., and Lakshminarayanan, V. (2022). Detection and Classification of Knee Osteoarthritis. Diagnostics, 12.10.3390/diagnostics12102362 ↗
45. Raisuddin, A.M., Nguyen, H.H., and Tiulpin, A. (2022, January 28–31). Deep Semi-Supervised Active Learning for Knee Osteoarthritis Severity Grading. Proceedings of the IEEE 19th International Symposium on Biomedical Imaging (ISBI), Kolkata, India. Available online: https://ieeexplore.ieee.org/document/9761668.10.1109/ISBI52829.2022.9761668 ↗
46. Development of an automated optimal distance feature-based decision system for diagnosing knee osteoarthritis using segmented X-ray images10.1016/j.heliyon.2023.e21703 ↗
47. Evaluating the efficacy of deep learning models for knee osteoarthritis prediction based on Kellgren-Lawrence grading system, e-Prime—Advances in Electrical Engineering
48. A Comparative Study of Machine Learning Classifiers for Enhancing Knee Osteoarthritis Diagn
49. Detection of Osteoarthritis in Knee Radiographic Images using Artificial Neural Network
50. Touahema, S., Zaimi, I., Zrira, N., Ngote, M.N., Doulhousne, H., and Aouial, M. (2024). MedKnee: A New Deep Learning-Based Software for Automated Prediction of Radiographic Knee Osteoarthritis. Diagnostics, 14.10.3390/diagnostics14100993 ↗
51. Knee osteoarthritis automatic detection using U-Net
52. Deep Learning Based Knee Osteoarthritis Detection and Classification10.48175/IJARSCT-15941 ↗
53. DNN-Based Knee OA Severity Prediction System: Pathologically Robust Feature Engineering Approach10.1007/s42979-022-01476-4 ↗
54. Detection and Classification of The Osteoarthritis in Knee Joint Using Transfer Learning with Convolutional Neural Networks (CNNs)
55. M, G.K., and Goswami, A.D. (2023). Automatic Classification of the Severity of Knee Osteoarthritis Using Enhanced Image Sharpening and CNN. Appl. Sci., 13.10.3390/app13031658 ↗
56. Knee osteoarthritis severity classification with ordinal regression module10.1007/s11042-021-10557-0 ↗
57. Automatic detection of knee osteoarthritis grading using artificial intelligence-based methods10.1002/ima.23057 ↗
58. Automated Detection For The Severity Of Knee Osteoarthritis from Plain Radiographs Using Machine Learning Methods
59. A warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening10.1177/1759720X21993254 ↗
60. Ensemble deep-learning networks for automated osteoarthritis grading in knee X-ray images10.1038/s41598-023-50210-4 ↗
61. Adversarial Evolving Neural Network for Longitudinal Knee Osteoarthritis Prediction10.1109/TMI.2022.3181060 ↗