Magnetic resonance and sonographic imagings of masticatory muscle myalgia in temporomandibular disorder patients

SummaryThis article reviews recently published studies investigating the MRI and sonographic diagnosis of masticatory muscle myalgia in temporomandibular disorder patients. The MRI and sonographic features of muscle after treatment are also discussed. Literature published within the last 15 years was obtained from the PubMed database using the following Mesh terms: magnetic resonance imaging (MRI) or sonography, masticatory muscle pain, and treatment. MRI and sonography enable accurate visualization and evaluation of the masticatory muscles, thereby increasing our understanding of pathology and cause of pain associated with these muscles. Although therapeutic efficacy is often evaluated based on clinical findings, MR and sonographic imaging studies may also be valuable

Preliminary Study on the Diagnostic Performance of a Deep Learning System for Submandibular Gland Inflammation Using Ultrasonography Images

This study was performed to evaluate the diagnostic performance of deep learning systems using ultrasonography (USG) images of the submandibular glands (SMGs) in three different conditions: obstructive sialoadenitis, Sjögren’s syndrome (SjS), and normal glands. Fifty USG images with a confirmed diagnosis of obstructive sialoadenitis, 50 USG images with a confirmed diagnosis of SjS, and 50 USG images with no SMG abnormalities were included in the study. The training group comprised 40 obstructive sialoadenitis images, 40 SjS images, and 40 control images, and the test group comprised 10 obstructive sialoadenitis images, 10 SjS images, and 10 control images for deep learning analysis. The performance of the deep learning system was calculated and compared between two experienced radiologists. The sensitivity of the deep learning system in the obstructive sialoadenitis group, SjS group, and control group was 55.0%, 83.0%, and 73.0%, respectively, and the total accuracy was 70.3%. The sensitivity of the two radiologists was 64.0%, 72.0%, and 86.0%, respectively, and the total accuracy was 74.0%. This study revealed that the deep learning system was more sensitive than experienced radiologists in diagnosing SjS in USG images of two case groups and a group of healthy subjects in inflammation of SMGs

Postoperative tomographic assessment of veneer bone grafting with implant placement in the maxillary anterior region.

Various ridge augmentation and sinus lift procedures were performed in severely resorbed alveolar crests of a maxilla to provide some volume for implant treatment. It was reported that the outcome of maxillary sinus lift procedures was evaluated with conventional tomography or computerized tomography, and that grafted bone around implants markedly progressed in resorption, particularly at the implant apex. However, veneer bone grafting with implant placement has not been evaluated after treatment with imaging techniques. Therefore, the purpose of this study was to assess veneer bone grafting after maxillary anterior implant treatment. Seven patients with a mean age of 24 years, with implants placed in the maxillary anterior region with or without autogenous veneer bone grafting were postoperatively examined using conventional tomography. On tomograms, the ratio of bone-to-implant contact and the area of bone were measured in labial bones with bone grafts, and they were compared with the values without bone grafts. In cases with bone grafting, the average ratio of bone-to-implant contact was 63.6%, whereas 81.8% was formed in cases without bone grafting. The average area of bone was 12.9 mm and 23.4 mm in patients with and without bone grafting, respectively. No significant difference was found between the implants with and without bone grafts. Resorbed labial bone was observed in the maxillary anterior region with and without veneer bone grafting

Evaluating the performance of generative adversarial network-synthesized periapical images in classifying C-shaped root canals

Abstract This study evaluated the performance of generative adversarial network (GAN)-synthesized periapical images for classifying C-shaped root canals, which are challenging to diagnose because of their complex morphology. GANs have emerged as a promising technique for generating realistic images, offering a potential solution for data augmentation in scenarios with limited training datasets. Periapical images were synthesized using the StyleGAN2-ADA framework, and their quality was evaluated based on the average Frechet inception distance (FID) and the visual Turing test. The average FID was found to be 35.353 (± 4.386) for synthesized C-shaped canal images and 25.471 (± 2.779) for non C-shaped canal images. The visual Turing test conducted by two radiologists on 100 randomly selected images revealed that distinguishing between real and synthetic images was difficult. These results indicate that GAN-synthesized images exhibit satisfactory visual quality. The classification performance of the neural network, when augmented with GAN data, showed improvements compared with using real data alone, and could be advantageous in addressing data conditions with class imbalance. GAN-generated images have proven to be an effective data augmentation method, addressing the limitations of limited training data and computational resources in diagnosing dental anomalies

Application of Deep Learning in the Identification of Cerebral Hemodynamics Data Obtained from Functional Near-Infrared Spectroscopy: A Preliminary Study of Pre- and Post-Tooth Clenching Assessment

In fields using functional near-infrared spectroscopy (fNIRS), there is a need for an easy-to-understand method that allows visual presentation and rapid analysis of data and test results. This preliminary study examined whether deep learning (DL) could be applied to the analysis of fNIRS-derived brain activity data. To create a visual presentation of the data, an imaging program was developed for the analysis of hemoglobin (Hb) data from the prefrontal cortex in healthy volunteers, obtained by fNIRS before and after tooth clenching. Three types of imaging data were prepared: oxygenated hemoglobin (oxy-Hb) data, deoxygenated hemoglobin (deoxy-Hb) data, and mixed data (using both oxy-Hb and deoxy-Hb data). To differentiate between rest and tooth clenching, a cross-validation test using the image data for DL and a convolutional neural network was performed. The network identification rate using Hb imaging data was relatively high (80‒90%). These results demonstrated that a method using DL for the assessment of fNIRS imaging data may provide a useful analysis system

Effects of 1 year training on the performance of ultrasonographic image interpretation: A preliminary evaluation using images of Sjogren syndrome patients.

Purpose This study investigated the effects of 1 year of training on imaging diagnosis, using static ultrasonography (US) salivary gland images of Sjögren syndrome patients. Materials and Methods This study involved 3 inexperienced radiologists with different levels of experience, who received training 1 or 2 days a week under the supervision of experienced radiologists. The training program included collecting patient histories and performing physical and imaging examinations for various maxillofacial diseases. The 3 radiologists (observers A, B, and C) evaluated 400 static US images of salivary glands twice at a 1-year interval. To compare their performance, 2 experienced radiologists evaluated the same images. Diagnostic performance was compared between the 2 evaluations using the area under the receiver operating characteristic curve (AUC). Results Observer A, who was participating in the training program for the second year, exhibited no significant difference in AUC between the first and second evaluations, with results consistently comparable to those of experienced radiologists. After 1 year of training, observer B showed significantly higher AUCs than before training. The diagnostic performance of observer B reached the level of experienced radiologists for parotid gland assessment, but differed for submandibular gland assessment. For observer C, who did not complete the training, there was no significant difference in the AUC between the first and second evaluations, both of which showed significant differences from those of the experienced radiologists. Conclusion These preliminary results suggest that the training program effectively helped inexperienced radiologists reach the level of experienced radiologists for US examinations