Temporomandibular Joint sounds are a very common disorder in the general population. Temporomandibular Disorder (TMD) is any discomfort related to Temporomandibular Joint (TMJ). In this paper, a novel decision support system based on deep learning and neural network algorithms for diagnosis of Temporomandibular Joint disorder is introduced. A non-invasive device is designed for the recording of TMJ sounds. An interface is developed that will facilitate the dentist to operate on the recorded audio data. The collected data consist of the patient's left and right Temporomandibular Joint sound, ambient noise sound, the patient's clinical data, the dentist's notes about the patient, diagnosis, and treatment. Then signal processing, artificial neural network and deep learning algorithms are used to classify these measurements, and thus, the method that decides about the patient's condition is developed. Frequency, statistical and deep learning-based methods are compared in terms of classification success. The results show that the classification success of the classification method based on deep learning is consistently over 94.5% and it is more successful than the previous two methods. The proposed system can give the physician an idea about the effectiveness of the treatment methods applied to the patient in order to treat joint sounds which are among the important symptoms of TMD.

颞下颌关节音是一般人群中非常常见的疾病。颞下颌关节紊乱症 (TMD) 是与颞下颌关节 (TMJ) 相关的任何不适。本文介绍了一种基于深度学习和神经网络算法的颞下颌关节疾病诊断决策支持系统。一种非侵入性设备专为记录 TMJ 声音而设计。开发了一个界面，便于牙医对记录的音频数据进行操作。采集的数据包括患者左右颞下颌关节声音、环境噪声声音、患者临床数据、牙医关于患者的笔记、诊断和治疗。然后使用信号处理、人工神经网络和深度学习算法对这些测量进行分类，因此，开发了决定患者状况的方法。频率、统计和基于深度学习的方法在分类成功方面进行了比较。结果表明，基于深度学习的分类方法的分类成功率始终在94.5%以上，比前两种方法更成功。所提出的系统可以让医生了解应用于患者的治疗方法的有效性，以便治疗属于 TMD 重要症状的关节声音。5%，比前两种方法更成功。所提出的系统可以让医生了解应用于患者的治疗方法的有效性，以便治疗属于 TMD 重要症状的关节声音。5%，比前两种方法更成功。所提出的系统可以让医生了解应用于患者的治疗方法的有效性，以便治疗属于 TMD 重要症状的关节声音。