PDF(Pubmed)
Abstract：
Temporomandibular disorders (TMDs) refer to a group of conditions that affect the temporomandibular joint, causing pain and dysfunction in the jaw joint and related muscles. The diagnosis of TMDs typically involves clinical assessment through operator-based physical examination, a self-reported questionnaire and imaging studies. To objectivize the measurement of TMD, this study aims at investigating the feasibility of using machine-learning algorithms fed with data gathered from low-cost and portable instruments to identify the presence of TMD in adult subjects. Through this aim, the experimental protocol involved fifty participants, equally distributed between TMD and healthy subjects, acting as a control group. The diagnosis of TMD was performed by a skilled operator through the typical clinical scale. Participants underwent a baropodometric analysis by using a pressure matrix and the evaluation of the cervical mobility through inertial sensors. Nine machine-learning algorithms belonging to support vector machine, k-nearest neighbours and decision tree algorithms were compared. The k-nearest neighbours algorithm based on cosine distance was found to be the best performing, achieving performances of 0.94, 0.94 and 0.08 for the accuracy, F1-score and G-index, respectively. These findings open the possibility of using such methodology to support the diagnosis of TMDs in clinical environments.
摘要：
颞下颌关节紊乱病（TMD）是指影响颞下颌关节的一组疾病，引起下颌关节和相关肌肉的疼痛和功能障碍。TMD的诊断通常涉及通过基于操作员的体格检查进行临床评估，自我报告问卷和影像学检查。为了客观地测量TMD，这项研究旨在调查使用机器学习算法的可行性,该算法结合了从低成本和便携式仪器收集的数据来识别成人受试者中TMD的存在.通过这个目标,实验方案涉及50名参与者，平均分布在TMD和健康受试者之间，作为对照组。TMD的诊断由熟练的操作者通过典型的临床量表进行。参与者通过使用压力矩阵进行了气压分析，并通过惯性传感器评估了颈椎的活动性。属于支持向量机的九种机器学习算法，比较了k近邻和决策树算法。基于余弦距离的k近邻算法被发现是性能最好的，精度达到0.94、0.94和0.08的性能，F1评分和G指数，分别。这些发现打开了使用这种方法来支持临床环境中TMD诊断的可能性。
