[Advances in prediction models for temporomandibular disorders]

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.