Advances in application of artificial intelligence in diagnosis and progress prediction of knee osteoarthritis
10.11855/j.issn.0577-7402.0023.2024.0307
- VernacularTitle:人工智能在膝骨关节炎诊疗中的应用进展
- Author:
Hai-Tao YU
1
;
Hao-Yue WU
;
Hao-Qiang ZHANG
;
Chen-Po DANG
;
Xu-Sheng LI
Author Information
1. 甘肃中医药大学第一临床医学院,甘肃 兰州 730030;解放军联勤保障部队第940医院关节外科,甘肃 兰州 730050
- Keywords:
osteoarthritis;
artificial intelligence;
machine learning;
deep learning
- From:
Medical Journal of Chinese People's Liberation Army
2025;50(1):9-15
- CountryChina
- Language:Chinese
-
Abstract:
Knee osteoarthritis(KOA)is a chronic degenerative joint disease,which poses a major challenge particularly among the elderly population due to its high incidence and high disability.Imaging examination has been used commonly to diagnose KOA.However,it faces imitations in predicting disease progression due to the lack of prior information and constraints in manpower and time.With the rapid evolution of big data and computational technologies,artificial intelligence(AI)is progressively integrating into various healthcare domains.Therefore,the integration of artificial intelligence(AI)into healthcare holds promise for revolutionizing KOA diagnosis and treatment.AI-assisted diagnostic models have demonstrated the potential to automate diagnosis,classify disease severity,and predict disease progression with improved efficiency and accuracy.In addition,these models provide personalized diagnosis and treatment options,as well as accurate disease progression risk assessment.Despite these promising outcomes,challenges such as high costs associated with data annotation and limitations in model generalization capabilities persist.This paper reviews recent advancements in AI applications and summarizes the potential value of utilizing AI applications for KOA.To further enhance the utilization of AI in KOA management to overcome current limitations,future efforts should focus on standardizing clinical sample databases,optimizing AI algorithms,and enhancing external verification sets.
