Knee Cartilage Degradation in the Medial and Lateral Anatomical Compartments: A Radiomics Study

Osteoarthritis (OA) is a prevalent form of arthritis, characterized by the degradation of joints hyaline cartilage, particularly the knee, which often necessitates surgical intervention due to its limited ability to self-healing. This study investigates the potential of using radiomics and machine learning (ML) for the automatic classification of degenerative and healthy knees in the medial and lateral anatomical compartments. A multimodal dataset comprising MRI and CT images of OA-diagnosed and healthy subjects was used. Image segmentation and registration were conducted using Mimics software, and radiomic features were extracted from CT and MRI scans. Feature selection via Recursive Feature Elimination (RFE) and ML algorithms were employed for classification. Results indicate superior classification performance in the medial compartment, both for MRI and CT images, suggesting their importance in OA diagnosis. This study contributes to advancing non-invasive OA diagnosis, with implications for personalized treatment strategies.