Effect of the impacted third molars on the development of temporomandibular joint (TMJ) clicking

Mischievous mandibular third molars camouflaging temporomandibular joint disorders

Objectives

The aim of this study was to observe the relationship between impacted mandibular third molars and development of temporomandibular joint (TMJ) disorders. Knowledge of the factors that have an adverse effect on the TMJ is necessary for proper diagnosis, treatment, and prognosis of TMJ disorders.

Materials and Methods

The study was performed on 80 patients aged between 20 and 60 years with impacted mandibular third molars, over a period of two months. The patients were examined clinically and radiologically to determine the type of impaction and detect the associated TMJ symptoms or disorders.

Results

In the 80 patients, 63.8% (51/80) of TMJ disorders were found in the horizontal group, 46.3% (37/80) in the mesioangular group, 42.5% (34/80) in the distoangular group, and 30.0% (24/80) in the vertical group of impacted mandibular third molars.

Conclusion

The study concluded that type of impacted mandibular third molar is factor in the development of temporomandibular disorders.

A Novel Method of Temporomandibular Joint Hypermobility Diagnosis Based on Signal Analysis

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.