Assessment Of Schoolchildren’s Temporomandibular Joint Sounds Associated With Bruxism

Arafa, Ahmed Fathy; Mostafa, Nawras Maher; Moussa, Shady Ahmed

doi:10.15226/jdodt.2019.001100

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…The Finnish researchers confirmed in their study that the results obtained from the RDC/TMD questionnaire coincide with the results obtained from new questionnaire DC/TMD [15]. Nowadays, one of the additional diagnostic tools commonly used for temporomandibular joints (TMJ) examination is an auscultation [16][17][18][19][20][21]. Auscultation is the basic diagnostic tool used by clinicians of various specialisations.…”

Section: Introductionsupporting

confidence: 52%

A proposal for a database of sounds generated by temporomandibular joints and a tool for automated diagnosis based on an RDC/TMD questionnaire

Grochala,

Kajor,

Pihut

et al. 2023

Folia Medica Cracoviensia

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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.

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Section: Introductionsupporting

confidence: 52%

A proposal for a database of sounds generated by temporomandibular joints and a tool for automated diagnosis based on an RDC/TMD questionnaire

Grochala,

Kajor,

Pihut

et al. 2023

Folia Medica Cracoviensia

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The Temporomandibular Joint and the Human Body: A New Perspective on Cross Talk

Abbass,

Rady,

El Moshy

et al. 2024

Dentistry Journal

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Background: As a unique joint that facilitates the articulation of the upper and lower jaws, the temporomandibular joint (TMJ) is concerned with several critical functions, such as speech and mastication. Pain that can become incapacitating is a result of temporomandibular disorders (TMDs), which are complex disorders affecting the masticatory muscles and the TMJ. Several anomalies and TMDs have an interdisciplinary relationship. Complementary and concurrent disorders may be caused by occlusal anomalies, psychological disorders, and changes in spine posture. Methods: This article examines the clinical characteristics of TMDs, their classification, their etiological factors, and the impact of TMJ disorders on the human body with reference to their anatomies and histological structures. Results: The clinical picture of some TMJ pathologies may be unknown, so certain biomarkers, such as cytokines, may be useful for an accurate diagnosis as they are frequently seen in TMJ disorders. Furthermore, novel therapeutic approaches that target pro-inflammatory cytokines and treat TMDs by using tissue engineering and regenerative medicine while permitting TMJ cartilage and bone regeneration may offer numerous benefits that require clinical translation. Conclusions: Implementation of recent modalities such as microvesicles and platelet-rich plasma in growth factors may provide a promising approach to enhance bone formation. In addition, we target different biological markers that give insights into the introduction of new pharmaceutical agents for therapy.

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New Methods for the Acoustic-Signal Segmentation of the Temporomandibular Joint

Kajor

Kucharski

Grochala

et al. 2022

JCM

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(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.

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Assessment Of Schoolchildren’s Temporomandibular Joint Sounds Associated With Bruxism

Cited by 4 publications

References 29 publications

A proposal for a database of sounds generated by temporomandibular joints and a tool for automated diagnosis based on an RDC/TMD questionnaire

A proposal for a database of sounds generated by temporomandibular joints and a tool for automated diagnosis based on an RDC/TMD questionnaire

The Temporomandibular Joint and the Human Body: A New Perspective on Cross Talk

New Methods for the Acoustic-Signal Segmentation of the Temporomandibular Joint

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