Department of Computer Science and Engineering, B.L.D.E.A's V.P.Dr.P.G. Halakatti College of Engineering and Technology, Vijayapur, Karnataka, India.
Department of Computer Science and Engineering, Scientific Collaborations for Developing Markets United Imaging Healthcare, Shanghai, China.
Curr Rheumatol Rev. 2024;20(2):133-156. doi: 10.2174/0115733971253574231002074759.
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.
膝骨关节炎（Knee Osteoarthritis，KOA）是一种以软骨丧失为特征的退行性关节疾病，可通过影像学手段观察到，并转化为影像学特征。老年人是膝骨关节炎的高发人群，全球 15 岁及以上人群中有 16%受到影响。由于软骨组织退化，老年人易患原发性膝骨关节炎。相比之下，年轻人的关节过度使用或创伤可能导致继发性膝骨关节炎。研究表明，早期发现膝骨关节炎可能是成功管理该疾病的策略。评分量表和分级系统是膝骨关节炎管理的重要工具，因为它们可以让临床医生衡量疾病严重程度的进展，并在确定的阶段提供合适治疗的建议。这项全面的研究综述了各种主观和客观的膝关节评估评分系统，这些系统可以根据关节软骨发生缺陷或变化的部位，有效地对 KOA 进行评分和分级。最近的研究表明，基于人工智能的方法，如 DenseNet，将深度学习的概念融入到 KOA 的评分和分级中，可以优于各种最先进的方法，从而实现早期预测 KOA。
