Advances in machine learning models for cervical spondylosis
- VernacularTitle:机器学习模型在颈椎病中的应用进展
- Author:
Wentong YANG
1
;
Jirong ZHAO
1
;
Xu XUE
1
;
Dong MA
1
;
Rui ZHAO
1
;
Junhao LIU
1
;
Boqian MA
1
Author Information
- Publication Type:Journal Article
- Keywords: cervical spondylosis; machine learning; deep learning; review
- From: Chinese Journal of Medical Physics 2025;42(2):269-273
- CountryChina
- Language:Chinese
- Abstract: The diagnosis,treatment,and prognosis evaluation of cervical spondylosis are challenging in clinic.Machine learning(ML)models can improve the accuracy and efficiency of cervical spondylosis diagnosis by processing complex clinical data,assist in selecting more precise treatment plans,and evaluate prognosis.Through the domestic and foreign literature review on the application of ML models in cervical spondylosis in recent years,the study classifies and summarizes the relevant models applied in the diagnosis,treatment,and prognosis evaluation of cervical spondylosis,introduces classic algorithms such as random forest,as well as new algorithms such as convolutional neural networks,deep neural networks and long short-term memory networks,aiming to provide reference ML solutions for various stages of cervical spondylosis diagnosis and treatment.
