Department of Physiotherapy and Rehabilitation, Faculty of Health Sciences, Karamanoglu Mehmetbey University, Karaman, Turkey.
Department of Mathematics, Kamil Özdağ Faculty of Science, Karamanoğlu Mehmetbey University, Karaman, Turkey.
Medicine (Baltimore). 2024 Oct 11;103(41):e39912. doi: 10.1097/MD.0000000000039912.
This study aimed to predict temporomandibular disorder (TMD) using machine learning (ML) approaches based on measurement parameters that are practically acquired in clinical settings. 125 patients with TMD and 103 individuals without TMD were included in the study. Pain intensity (with visual analog scale), maximum mouth opening (MMO) and lateral excursion movements (with millimeter ruler), cervical range of motion (with goniometer), pressure pain threshold (PPT; with algometer), oral parafunctional behaviors (with Oral Behaviors Checklist), psychological status (with Hospital Anxiety and Depression Scale), and quality of life (with Oral Health Impact Profile) were evaluated. The measurements were analyzed via over 20 ML algorithms, taking into account an extensive parameter tuning and cross-validation process. Results of variable importance were also provided. Bagging algorithm using Multivariate Adaptive Regression Spline (MARS) algorithm (accuracy = 0.8966, area under receiver operating characteristic curve = 0.9387, F1-score = 0.9032) was the best performing model regarding the performance criteria. According to this model, the 5 most important variables for predicting TMD were pain intensity, MMO, lateral excursion and PPT values of masseter and temporalis anterior muscles, respectively. The Bagging algorithm using the MARS algorithm is a robust model that, in combination with clinical parameters, assists in the detection of patients with TMD in settings with limited capabilities. The clinical parameters and ML algorithm proposed in this study may assist clinicians inexperienced in TMD to make a preliminary detection of TMD in clinics where diagnostic imaging tools are limited.
本研究旨在使用基于临床实际获取的测量参数的机器学习 (ML) 方法预测颞下颌关节紊乱症（TMD）。共纳入 125 例 TMD 患者和 103 例无 TMD 患者。评估疼痛强度（采用视觉模拟评分法）、最大张口度（MMO）和侧向运动（采用毫米尺）、颈椎活动度（采用关节角度计）、压痛阈（采用压痛计）、口腔功能障碍行为（采用口腔行为检查表）、心理状态（采用医院焦虑抑郁量表）和生活质量（采用口腔健康影响量表）。通过 20 多种 ML 算法对测量结果进行分析，并考虑了广泛的参数调整和交叉验证过程。还提供了变量重要性的结果。基于多元自适应回归样条（MARS）算法的袋装算法（准确率=0.8966，接收者操作特征曲线下面积=0.9387，F1 分数=0.9032）在性能标准方面表现最佳。根据该模型，预测 TMD 的 5 个最重要变量分别是疼痛强度、MMO、咀嚼肌和颞肌的侧向运动和 PPT 值。基于 MARS 算法的袋装算法是一种稳健的模型，结合临床参数可辅助在能力有限的环境中检测 TMD 患者。本研究提出的临床参数和 ML 算法可能有助于缺乏 TMD 经验的临床医生在诊断影像学工具有限的诊所中对 TMD 进行初步检测。
