Ultrasonographic and electromyographic evaluation of three types of occlusal splints on masticatory muscle activity, thickness, and length in patients with bruxism.

OBJECTIVE: To evaluate occlusal splint type differences in patients with bruxism. METHODS: Seventeen controls and 51 patients were divided into three subgroups, each assigned to use a different occlusal splint (hard, soft, or semi-soft) for 3 months and assessed by ultrasonography and electromyography (EMG) before (BT) and 3 months after treatment (AT). RESULTS: EMG values in all of the occlusal splint groups were significantly lower AT than BT (p < 0.05). BT and AT EMG values in the control group did not differ. Mean muscle thicknesses in bruxism patients was greater than in controls, and the greatest muscle thickness changes occurred with the hard occlusal splint (p < 0.05). DISCUSSION: A decrease in EMG activity occurred with all three splint types and was most prominent in the hard occlusal splint group. Ultrasonographic measurements of muscle length and thickness should be used alongside EMG to measure muscle activity in bruxism patients.

A U-Net Approach to Apical Lesion Segmentation on Panoramic Radiographs.

The purpose of the paper was the assessment of the success of an artificial intelligence (AI) algorithm formed on a deep-convolutional neural network (D-CNN) model for the segmentation of apical lesions on dental panoramic radiographs. A total of 470 anonymized panoramic radiographs were used to progress the D-CNN AI model based on the U-Net algorithm (CranioCatch, Eskisehir, Turkey) for the segmentation of apical lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Eskisehir Osmangazi University. A U-Net implemented with PyTorch model (version 1.4.0) was used for the segmentation of apical lesions. In the test data set, the AI model segmented 63 periapical lesions on 47 panoramic radiographs. The sensitivity, precision, and F1-score for segmentation of periapical lesions at 70% IoU values were 0.92, 0.84, and 0.88, respectively. AI systems have the potential to overcome clinical problems. AI may facilitate the assessment of periapical pathology based on panoramic radiographs.