Automated diagnostic system for classification of progression stages of osteoarthritis using magnetic resonance imaging.
Authors
Affiliations (3)
Affiliations (3)
- Department of Computer Science & Engineering, Tripura University (A Central University), Suryamaninagar 799022, Tripura, India.
- Department of Physical Medicine and Rehabilitation, Agartala Government Medical College and G B Pant Hospital, Kunjaban, Agartala 799006, Tripura, India.
- Department of Computer Science & Engineering, Tripura University (A Central University), Suryamaninagar 799022, Tripura, India. Electronic address: [email protected].
Abstract
Osteoarthritis (OA) is a degenerative joint disease characterized by cartilage loss, synovial fluid imbalance, and bone structural changes, leading to reduced mobility. Most clinical studies use MRI-derived cartilage characteristics to assess OA progression. To support timely treatment decisions and minimize human error, an automated computer aided system is needed for prediction of OA in the progressive stages. To build the automatic system for classifying progression phases of OA, we present a novel hybrid framework that incorporates cartilage characteristics of longitudinal knee MRI images from OAI dataset. The framework consists of two key phases: (i) initially, cartilage regions were segmented using an attention-enhanced position-aware encoder-decoder network. Multiple attention mechanisms were investigated at different network locations to identify the optimal segmentation strategy, and (ii) Morphological shape features were extracted from the segmented from the segmented MRI images. Statistical analysis was performed to select the most discriminative features, which were then used to classify OA progression stages corresponding to 18-month and 30-month follow-up examinations using machine learning classifiers. The results demonstrated that the proposed framework effectively classified OA progression at both follow-up periods. Among all classifiers, Random Forest achieved the best performance with an average F-measure of 84.13%, specificity of 87.65%, sensitivity of 96.15%, and accuracy of 86.67% across all folds. Overall, the approach attained average accuracy and F-measure of 76.50% and 71.84% respectively. The effectiveness of this framework suggests its potential as an automatic decision-support system to assist clinicians in making accurate and consistent OA diagnosis.