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Artificial Intelligence in the Detection of Knee Joint Injuries: A Comprehensive Review
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3
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2025
Jahr
Abstract
Knee joint injuries are a leading cause of musculoskeletal disorders, affecting millions worldwide. Traditional diagnostic methods, such as clinical examination and radiographic imaging, often have constraints in terms of performance parameters. Past one-decade, Artificial Intelligence (AI) has shown immense potential in enhancing the performing of diagnosis of knee joint injuries. The Machine Learning (ML) and Deep Learning (DL) models are applied to medical images from modalities viz. X-ray, MRI, and CT scans for the detection of knee disorders. Common knee injuries include knee osteoarthritis (KOA), Anterior Cruciate Ligament (ACL) tears, meniscal injuries, and fractures. This paper reviews the current state of AI applications in knee joint injury detection, AI models used, their advantages, challenges, and prospects. The findings suggest that AI can significantly enhance diagnostic performance, decrease detection time, speedup clinical decision-making, despite challenges related to data quality, interpretability, and generalization.
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