A COMPREHENSIVE ANALYSIS OF DEEP LEARNING TECHNIQUES FOR CLASSIFYING KNEE ABNORMALITIES

Hirenkumar Kukadiya; Divyakant Meva

doi:10.29121/granthaalayah.v13.i3.2025.5992

Authors

Dr. Hirenkumar Kukadiya Gandhinagar Institute of Computer Science and Applications, Gandhinagar University, Gandhinagar (Gujarat) 382721, India
Dr. Divyakant Meva Department of Computer Application, Marwadi University, Rajkot (Gujarat) 360003, India

DOI:

https://doi.org/10.29121/granthaalayah.v13.i3.2025.5992

Keywords:

Computer-Aided Diagnosis, Classification, Convolutional Neural Networks, Deep Learning, Knee Abnormalities, Medical imaging, MRI

Abstract [English]

Knee abnormalities represent one of the most common orthopedic conditions affecting individuals across different age groups, significantly impacting mobility and quality of life. For treatment planning to be successful, these anomalies must be diagnosed promptly and accurately. Deep learning techniques have transformed medical image analysis in the last ten years, providing promising answers for automated knee anomaly classification from a variety of imaging modalities. This comprehensive review examines the current state-of-the-art deep learning techniques for knee abnormality classification, analyzing their architectures, performance metrics, clinical applications, and limitations. We systematically categorize these approaches based on the imaging modalities used (MRI, X-ray, ultrasound), the specific knee abnormalities targeted, and the underlying deep learning architectures employed. Additionally, we discuss the challenges in this field, including limited dataset availability, class imbalance, interpretability issues, and the gap between research and clinical implementation. Finally, we highlight emerging trends and future research directions that could further enhance the clinical utility of deep learning for knee abnormality classification.

Downloads

Download data is not yet available.

References

Antony, J., McGuinness, K., Connor, N.E., & Moran, K. (2020). Quantifying Radiographic Knee Osteoarthritis Severity using Deep Convolutional Neural Networks. IEEE Conference on Computer Vision and Pattern Recognition Workshops, 45-53. https://doi.org/10.1109/ICPR.2016.7899799 DOI: https://doi.org/10.1109/ICPR.2016.7899799

Bien, N., Rajpurkar, P., Ball, R.L., & Langlotz, C.P. (2018). Deep-Learning-Assisted Diagnosis for Knee Magnetic Resonance Imaging: Development and Retrospective Validation of MRNet. PLOS Medicine, 15(11), e1002699. https://doi.org/10.1371/journal.pmed.1002699 DOI: https://doi.org/10.1371/journal.pmed.1002699

Chen, J., Wu, L., Zhang, Y., & Wang, X. (2022). Multi-View Ensemble Learning for Anterior Cruciate Ligament Tear Detection from Knee MRI. Medical Image Analysis, 78, 102382.

Fritz, B., Marbach, G., Civardi, F., & Sutter, R. (2022). Deep Learning Detection and Classification of Meniscal Tears in MR Imaging of the Knee with Minimal User Input. Radiology: Artificial Intelligence, 4(3), e210215.

García-Castro, F., Salamanca, L., Tornero, E., & Montseny, E. (2024). Joint Segmentation and Classification of Knee Structures Through Multi-Task Learning. IEEE Transactions on Medical Imaging, 43(1), 45-57.

Kim, S.H., Jung, H.Y., Park, J.W., & Lee, S.C. (2021). Deep Learning-Based Classification of Meniscal Tears in Ultrasound Imaging. Ultrasound in Medicine & Biology, 47(3), 382-391.

Kumar, D., Shaikh, A., Zhang, W., & Brown, M.S. (2023). 3D DenseNet with Spatial Attention for Anterior Cruciate Ligament Tear Classification. Computerized Medical Imaging and Graphics, 108, 102174.

Lee, J., Kim, H., Choi, Y., & Park, S. (2023). Dual-Stream Deep Network for Multimodal Knee Osteoarthritis Assessment Using MRI and Radiography. Journal of Biomedical Informatics, 140, 104328.

Leung, K.K., Zhang, J., Tan, W.L., & Soh, J.Y. (2022). Weakly Supervised Learning for Radiographic Osteoarthritis Progression Prediction. Medical Image Analysis, 81, 102541.

Liu, B., Luo, J., Huang, H., & Zhang, L. (2023). DenseNet with Bidirectional ConvLSTM for Dynamic Knee MRI Interpretation. Computer Methods and Programs in Biomedicine, 227, 107241.

Liu, F., Zhou, Z., Samsonov, A., & Kijowski, R. (2021). Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection. Radiology, 298(3), 620-629.

Park, C.H., Lee, K.J., Cho, S.H., & Kim, T.Y. (2024). Multimodal Fusion of Imaging and Clinical Features for Enhanced Knee Meniscal Tear Classification. Journal of Digital Imaging, 37(1), 189-201.

Pedoia, V., Norman, B., Mehany, S.N., & Majumdar, S. (2019). 3D Convolutional Neural Networks for Detection and Severity Staging of Meniscus and PFJ Cartilage Morphological Degenerative Changes in Osteoarthritis and Anterior Cruciate Ligament Subjects. Journal of Magnetic Resonance Imaging, 49(2), 400-410. https://doi.org/10.1002/jmri.26246 DOI: https://doi.org/10.1002/jmri.26246

Raza, M., Siddiqui, S.A., Zafar, H., & Ahmad, A. (2022). Transfer Learning-Based Approach for Knee Ligament Tear Classification from Ultrasound Images. IEEE Journal of Biomedical and Health Informatics, 26(6), 2708-2719.

Roblot, V., Giret, Y., Bou Antoun, M., & Cotten, A. (2020). Artificial Intelligence to Diagnose Meniscus Tears on MRI. Diagnostic and Interventional Imaging, 101(3), 111-118. https://doi.org/10.1016/j.diii.2019.02.007 DOI: https://doi.org/10.1016/j.diii.2019.02.007

Tiulpin, A., Thevenot, J., Rahtu, E., & Saarakkala, S. (2019). A Novel Method for Automatic Localization and Detection of Traumatic Knee Injuries from MR Images using the Siamese Neural Network. International Journal of Computer Assisted Radiology and Surgery, 14(7), 1073-1082.

Tsai, C.H., Kiryati, N., Konen, E., & Eshed, I. (2022). Bounding Box Approach with Deep Learning for Automatic Meniscus Detection and Classification from Knee MRI Examinations. European Radiology, 32(3), 1620-1628.

Wang, L., Lin, Z.Q., Wong, A., & Chung, A.G. (2023). Hierarchical Deep Learning Framework for Multi-class Knee Abnormality Classification from MRI. Scientific Reports, 13(1), 1-15.

Zhang, K., Liu, X., Fang, Z., & Wu, G. (2020). 3D CNN with Attention Mechanism for Meniscal Tear Detection on Knee MRI. IEEE Transactions on Medical Imaging, 39(8), 2594-2604. DOI: https://doi.org/10.1109/TMI.2020.2996256

Zhang, Y., Gorriz, M., Burgos, N., & Lekadir, K. (2023). Transformer-Based Approach for Early Osteoarthritis Feature Detection from Knee Radiographs. Computers in Biology and Medicine, 156, 106629.