摘要
Abstract
Knee osteoarthritis(KOA)is a common chronic degenerative joint disease,particularly prevalent among the elderly,which has high morbidity and disability rates.In recent years,artificial intelligence(AI)technology has advanced rapidly,and machine learning,as one of its key applications,has demonstrated significant potential in the field of medical image analysis,especially in the radiological diagnosis and clinical prediction of KOA.Specifically,machine learning algorithms have been applied to several critical aspects of KOA,including automatic grading,lesion detection,prognosis prediction,and risk assessment.Deep learning models have been proven capable of automatically extracting features from knee X-ray or MRI images and achieving accurate KOA grading,even outperforming traditional manual feature-based methods.Additionally,machine learning has been employed to predict disease progression and treatment outcomes in KOA,providing important insights for the development of personalized treatment plans.Nevertheless,current applications still face challenges such as inconsistent data quality and insufficient model generalizability.This review summarizes the advances in machine learning applications for KOA diagnosis and prediction,with a focus on analyzing the specific contributions of existing algorithms in automatic grading,lesion detection,and prognosis prediction.It also delves into the limitations of current applications.To further advance the field,this paper proposes measures such as establishing standardized clinical sample databases,continuously optimizing algorithms,and strengthening external validation.These efforts aim to provide new insights and methods for the early diagnosis,precision treatment,and effective management of KOA.关键词
人工智能/膝骨关节炎/机器学习/深度学习Key words
Artificial intelligence/Knee osteoarthritis/Machine learning/Deep learning分类
医药卫生
