Advances in prediction models for temporomandibular disorders
10.3760/cma.j.cn112144-20250401-00114
- VernacularTitle:颞下颌关节紊乱病预测模型的研究进展
- Author:
Yueran ZHANG
1
;
Yunuo ZHOU
1
;
Jingyi HUANG
1
;
Wei FANG
1
Author Information
1. 武汉大学口腔医（学）院口腔颌面创伤与颞下颌关节外科，武汉 430079
- Publication Type:Journal Article
- Keywords:
Temporomandibular joint;
Temporomandibular disorders;
Traditional statistics;
Machine learning;
Deep learning;
Prediction models
- From:
Chinese Journal of Stomatology
2025;60(7):787-792
- CountryChina
- Language:Chinese
-
Abstract:
Temporomandibular disorders (TMD), a common condition in oral and maxillofacial surgery, significantly impairs patients' quality of life. Early prediction and appropriate treatment of TMD are therefore critically important. Research on TMD prediction models has evolved from traditional statistical methods to machine learning and subsequently to deep learning, each offering unique advantages and limitations. Traditional statistical methods can effectively identify independent risk factors influencing treatment outcomes but generally rely on substantial prior knowledge and assumptions. Machine learning techniques can process large-scale, high-dimensional data and autonomously learning patterns and regularities within datasets. However, they exhibit strong dependence on data quality and limited model generalization capabilities. Deep learning approaches excel at automatically extracting temporal patterns and trends from time-series data while effectively capturing complex nonlinear relationships, yet they require extensive training datasets and suffer from interpretability challenges due to their inherent black-box testing. This review comprehensively evaluates the implementation and performance of these computational approaches in TMD prediction, critically analyzes their respective strengths and constraints, and discusses promising future research directions.
