Comprehensive Study on Scoring and Grading Systems for Predicting the
Severity of Knee Osteoarthritis

Pavan      Mahendrakar; Dileep      Kumar; Uttam      Patil
doi:10.2174/0115733971253574231002074759
Abstract

Knee Osteoarthritis (KOA) is a degenerative joint ailment characterized by cartilage loss, which can be seen using imaging modalities and converted into imaging features. The older population is the most affected by knee OA, which affects 16% of people worldwide who are 15 years of age and older. Due to cartilage tissue degradation, primary knee OA develops in older people. In contrast, joint overuse or trauma in younger people can cause secondary knee OA. Early identification of knee OA, according to research, may be a successful management tactic for the condition. Scoring scales and grading systems are important tools for the management of knee osteoarthritis as they allow clinicians to measure the progression of the disease's severity and provide suggestions on suitable treatment at identified stages. The comprehensive study reviews various subjective and objective knee evaluation scoring systems that effectively score and grade the KOA based on where defects or changes in articular cartilage occur. Recent studies reveal that AI-based approaches, such as that of DenseNet, integrating the concept of deep learning for scoring and grading the KOA, outperform various state-of-the-art methods in order to predict the KOA at an early stage.
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DOI https://dx.doi.org/10.2174/0115733971253574231002074759	Print ISSN 1573-3971
Publisher Name Bentham Science Publisher	Online ISSN 1875-6360
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