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.

Introduction. 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 of the study. To assess the effectiveness of teaching students of the fifth year of dentistry how to perform theclinical examination of the masticatory system in accordance with the RDC/ TMD questionnaire.Material and methods. Fifty-five students of dentistry took part in the study. The study protocol contained theoretical information on the RDC/TMD examination axis I clinical procedures presented during a lecture. During a seminar, the examination rules were demonstrated. Groups of four to six students were then presented with the practical manual procedure. These groups were examined using the RDC/TMD procedure by a teaching dentist and a student. The examination Effectiveness of teaching undergraduate students how to perform temporomandibular disorder examinationsSkuteczność nauczania studentów procedury badania zaburzeń skroniowo-żuchwowych

The temporomandibular joint (TMJ), being an almost well-known anatomical structure but its diagnosis may become difficult due to sounds accompanying joint movement. One example is temporomandibular joint hypermobility (TMJH), which still requires comprehensive study. TMJH is a rare disorder; however, its prevalence at the level of around 4% is still significant. We propose a diagnostic method of TMJH based on the digital time-frequency analysis of sounds generated by TMJ. The volunteers were diagnosed using the RDC/TMD questionnaire and auscultated with the Littmann 3200 electronic stethoscopes on both sides of the head simultaneously. Recorded TMJ sounds were transferred to the computer via Bluetooth® for numerical analysis. The research reveals characteristic time-frequency features in acoustic signals which can be used to detect TMJH. This can help differentiate other disc displacements from joint hypermobility.

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