Comparison of detection performance of soft tissue calcifications using artificial intelligence in panoramic radiography.

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.

Effect of decompression for patients with various jaw cysts based on a three-dimensional computed tomography analysis.

OBJECTIVES: The aim of this study is to evaluate the effectiveness of decompression according to reduction parameters by measuring cystic volume changes using computed tomography (CT). STUDY DESIGN: We chose patients who had undergone decompression for a cystic lesion of the jaw at the Department of Oral and Maxillofacial Surgery in our dental hospital between 2006 and 2015. All patients had been examined by CT before and after decompression. We measured the volume of cystic lesions using the threshold method and evaluated the volumetric changes after decompression according to various parameters. RESULTS: The mean reduction rates of 46 cystic cases was 57.95%. There was a statistically significant difference in reduction rates by initial volume; the group with large initial volume reported higher reduction rates. CONCLUSION: Decompression is more effective in larger initial lesions when carried out more than 6 months. CT is an efficient method for evaluating the bony healing within the cyst cavity by measuring changes in cystic volume after decompression. However, due to its high cost and radiation dose, a comparative study using another diagnostic radiographic tool should be considered.

Mandibular metastasis of cholangiocarcinoma: A case report.

Tumors metastasizing from distant regions to the oral and maxillofacial region are uncommon, comprising only 1%-2% of all malignancies. Cholangiocarcinoma is a malignancy that arises from cholangiocytes, which are epithelial cells that line the bile ducts. These cancers are difficult to diagnose and have a poor prognosis. In this paper, we report a rare case of mandibular metastasis of cholangiocarcinoma diagnosed at the primary site and discuss the radiographic findings observed in this case.