Abstract
Osteoarthritis (OA) presents a significant global health burden. Artificial Intelligence (AI) and Machine Learning (ML) techniques are potentially helpful tools for OA research and clinical management. Additionally, the integration of non-imaging clinical data and radiomics and texture features further enhances diagnostic accuracy and prognostic assessment. Despite notable progress, challenges such as dataset bias and generalizability issues persist, requiring collaborative efforts for broader clinical implementation. Nonetheless, AI-driven approaches are useful tools in OA diagnosis, prognosis, and treatment evaluation, which finally make these tasks easier and less time-consuming for physician in management of this prevalent musculoskeletal disorder. This comprehensive review explores the role of AI in OA, focusing on ML methods, particularly Convolutional Neural Networks (CNNs), in tasks such as classification, segmentation, diagnosis, and prognosis.
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