The correlation between spiral CT and physical measurement was high except in sites of very thin mucosa. Spiral CT can be considered an alternative method for the measurement of oral mucosal thickness. Because of the higher radiation exposure, caution should be exercised and radiation dosage versus clinical benefit assessment is required.

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Objectives This study determined suitable conditions for masseter and temporal muscle massage using a specially fabricated robot and evaluated its effects on patients with TMJ dysfunction associated with myofascial pain. Methods The robot was designed with two arms with six degrees-of-freedom, and equipped with plungers. A phase-1 trial examined 22 healthy volunteers to determine its safety and suitable massage pressure, examining three different pressures. The volunteers evaluated their comfort, warmth, and ease of mouth opening by use of a visual analogue scale (VAS). A phase-2 trial examined the safety, suitable dose regimen, and efficacy in 12 patients. Maximal mouth opening was measured, and muscle pain and massage were evaluated subjectively. Results The robot was safe in the phase-1 trial, except for two massages in which the pressure was excessive. Massages at 6-10 N were given the highest VAS scores. In phase 2, the massage pressure was arbitrary and each muscle was massaged seven times for 1 min, three times every two weeks. After evaluating the efficacy, additional treatments were performed at a greater pressure or for longer. The massage treatment was very effective for most patients. Conclusion The massage treatment was safe and effective for most patients when administered at a pressure of 6-10 N seven times for 1 min per muscle every two weeks. The robot may constitute a useful tool for treating TMJ dysfunction associated with myofascial pain.

CT clearly demonstrated characteristic features of odontogenic myxoma. CT analysis may contribute to establishing a consensus regarding the interpretation of conventional radiographic appearances in odontogenic myxoma.

Objectives: The aims of the present study were to construct a deep learning model for automatic segmentation of the temporomandibular joint (TMJ) disc on magnetic resonance (MR) images, and to evaluate the performances using the internal and external test data. Methods: In total, 1200 MR images of closed and open mouth positions in patients with temporomandibular disorder (TMD) were collected from two hospitals (Hospitals A and B). The training and validation data comprised 1000 images from Hospital A, which were used to create a segmentation model. The performance was evaluated using 200 images from Hospital A (internal validity test) and 200 images from Hospital B (external validity test). Results: Although the analysis of performance determined with data from Hospital B showed low recall (sensitivity), compared with the performance determined with data from Hospital A, both performances were above 80%. Precision (positive predictive value) was lower when test data from Hospital A were used for the position of anterior disc displacement. According to the intra-articular TMD classification, the proportions of accurately assigned TMJs were higher when using images from Hospital A than when using images from Hospital B. Conclusion: The segmentation deep learning model created in this study may be useful for identifying disc positions on MR images.