Artificial intelligence (AI) is limited to teeth and periodontal disease in the dental field, and is used for diagnosis assistance or data analysis, and there has been no research conducted in actual clinical situations. So, we created an environment similar to actual clinical practice and conducted research by selecting three of the soft tissue diseases (carotid artery calcification, lymph node calcification, and sialolith) that are difficult for general dentists to see. Therefore, in this study, the accuracy and reading time are evaluated using panoramic images and AI. A total of 20,000 panoramic images including three diseases were used to develop and train a fast R-CNN model. To compare the performance of the developed model, two oral and maxillofacial radiologists (OMRs) and two general dentists (GDs) read 352 images, excluding the panoramic images used in development for soft tissue calcification diagnosis. On the first visit, the observers read images without AI; on the second visit, the same observers used AI to read the same image. The diagnostic accuracy and specificity for soft tissue calcification of AI were high from 0.727 to 0.926 and from 0.171 to 1.000, whereas the sensitivity for lymph node calcification and sialolith were low at 0.250 and 0.188, respectively. The reading time of AI increased in the GD group (619 to 1049) and decreased in the OMR group (1347 to 1372). In addition, reading scores increased in both groups (GD from 11.4 to 39.8 and OMR from 3.4 to 10.8). Using AI, although the detection sensitivity of sialolith and lymph node calcification was lower than that of carotid artery calcification, the total reading time of the OMR specialists was reduced and the GDs reading accuracy was improved. The AI used in this study helped to improve the diagnostic accuracy of the GD group, who were not familiar with the soft tissue calcification diagnosis, but more data sets are needed to improve the detection performance of the two diseases with low sensitivity of AI.

The aim of this study was to compare changes of bite force, occlusal contact area, and dynamic functional occlusion analysis after occlusal stabilization splint therapy during sleep for one month in a patient with bruxism. Materials and Methods: From October 2021 to July 2022, sleep bruxism of 30 patients who visited the Department of Oral Medicine at Yonsei University College of Dentistry Hospital were recruited. The participants were divided into two groups: using an occlusal stabilization splint during sleep (treatment; n = 15) and not using an occlusal stabilization splint (control; n = 15). Before using the occlusal stabilization splint and one month after, bite force, occlusal contact area and dynamic functional occlusion analysis (ratio of left/right bite forces, average bite forces, maximum bite forces, and maximum contact areas during lateral and anterior and posterior mandibular movements) were performed. Results: There was no difference in bite force and occlusal contact area between the treatment group using the occlusal stabilization splint and the control group not using the occlusal stabilization splint during sleep for one month. However, there were significant differences in the average bite force and maximum bite force in the lateral and anterior and posterior mandibular movements and the maximum contact areas in the anterior and posterior mandibular movements. Conclusion:The occlusal stabilization splint is helpful for sleep bruxism patients who lateral and anterior and posterior mandibular movements. In addition, further studies are needed a double-blind study with a large population.