Despite the temporomandibular joint (TMJ) being a well-known anatomical structure its diagnosis may become difficult because physiological sounds accompanying joint movement can falsely indicate pathological symptoms. One example of such a situation is temporomandibular joint hypermobility (TMJH), which still requires comprehensive study. The commonly used official research diagnostic criteria for temporomandibular disorders (RDC/TMD) does not support the recognition of TMJH. Therefore, in this paper the authors propose a novel diagnostic method of TMJH based on the digital time–frequency analysis of sounds generated by TMJ. Forty-seven volunteers were diagnosed using the RDC/TMD questionnaire and auscultated with the Littmann 3200 electronic stethoscope on both sides of the head simultaneously. Recorded TMJ sounds were transferred to the computer via Bluetooth^® for numerical analysis. The representation of the signals in the time–frequency domain was computed with the use of the Python Numpy and Matplotlib libraries and short-time Fourier transform. The research reveals characteristic time–frequency features in acoustic signals which can be used to detect TMJH. It is also proved that TMJH is a rare disorder; however, its prevalence at the level of around 4% is still significant.

(1) Background: The stethoscope is one of the main accessory tools in the diagnosis of temporomandibular joint disorders (TMD). However, the clinical auscultation of the masticatory system still lacks computer-aided support, which would decrease the time needed for each diagnosis. This can be achieved with digital signal processing and classification algorithms. The segmentation of acoustic signals is usually the first step in many sound processing methodologies. We postulate that it is possible to implement the automatic segmentation of the acoustic signals of the temporomandibular joint (TMJ), which can contribute to the development of advanced TMD classification algorithms. (2) Methods: In this paper, we compare two different methods for the segmentation of TMJ sounds which are used in diagnosis of the masticatory system. The first method is based solely on digital signal processing (DSP) and includes filtering and envelope calculation. The second method takes advantage of a deep learning approach established on a U-Net neural network, combined with long short-term memory (LSTM) architecture. (3) Results: Both developed methods were validated against our own TMJ sound database created from the signals recorded with an electronic stethoscope during a clinical diagnostic trail of TMJ. The Dice score of the DSP method was 0.86 and the sensitivity was 0.91; for the deep learning approach, Dice score was 0.85 and there was a sensitivity of 0.98. (4) Conclusions: The presented results indicate that with the use of signal processing and deep learning, it is possible to automatically segment the TMJ sounds into sections of diagnostic value. Such methods can provide representative data for the development of TMD classification algorithms.

The prevalence of TMD indicates a need to develop new tools that are useful in the case of screening examinations. These methods can support diagnosis at the early stage of the disorder. The purpose of this research was to develop a comprehensive tool that would function as both a database of sounds generated by TMJ and as software which facilitates automated diagnosis. The software would also use the data from the RDC/TMD questionnaire. Such a tool may significantly reduce the time spent by dentists on making manual RDC/TMD diagnoses. Moreover, this solution would enable dentists who do not specialise in TMD to make effective diagnoses. 95 patient took part in the clinical examination: 30 man and 65 females. The mean age of the participants was 33 years. Patients participating in the clinical process were examined according to the Polish version of the RDC/TMD questionnaire (Axis I and Axis II). Subsequently, all subjects were auscultated with an electronic stethoscope. An application has been im-plemented based on the RDC/TMD diagnostic flow chart. This tool was used for the automated generation of RDC/TMD diagnoses for all patients. As a result of the kind permission and participation of the patients under examination, it was possible to store records of ninety-five people. Each record contains RDC/TMD questionnaire data, auscultation signals and RDC/TMD diagnoses. For the first time, a database was created that has the potential to facilitate further examination. However, the developed system is universal and can therefore be adapted to new DC/TMD criteria.

Background: Temporomandibular disorders (TMD) and headaches (HA) are conditions that often coexist in patients. HA could be related to functional disorders and, at the same time it is often idiopathic. Aim of study: To determine the cooccurrence of HA and TMD in adolescents in the same age...

Background: Difficulties in examination of the masticatory muscles and temporomandibular joints by students of dental faculties were an inspiration to introduce and teach the protocol according to the standardized RDC/TMD questionnaire. Aim: To assess the effectiveness of teaching...