[Progress on 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, with each phase offering distinct contributions and limitations. Traditional statistical methods can accurately identify independent risk factors affecting treatment efficacy but generally rely on substantial prior knowledge and assumptions. Machine learning techniques are capable of processing 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 synthesizes the applications and outcomes of these methodologies in TMD research, analyzes their respective strengths and constraints, and explores future directions for advancements in this field.