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.

Modified uvulopalatopharyngoplasty for the treatment of obstructive sleep apnea-hypopnea syndrome: resection of the musculus uvulae.

OBJECTIVE: To assess the outcome of a modified uvulopalatopharynbgoplasty (UPPP) technique with preservation of the uvula mucosa and partial resection of the musculus uvula. STUDY DESIGN AND METHODS: A prospective randomized controlled trial. Sixteen male patients underwent a classic UPPP, and another 16 male patients underwent a modified UPPP. The parameters evaluated were the subjective symptom score, the Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale (ESS), the Stanford Sleepiness Scale (SSS), and polysomnography results. RESULTS: Postoperative snoring, apnea, morning headache, daytime sleepiness, pharyngeal foreign body sensation, and global discomfort were not significantly different between the two groups. Among seven subjective measures of sleep quality from the PSQI, the subjective sleep quality was significantly improved in both groups. Postoperatively, the SSS was not significantly changed in both groups; however, the ESS was significantly improved in both groups. The polysomnographic findings showed a statistically significantly improvement in the apnea index and the apnea-hypopnea index. CONCLUSION: Although the modified group did not have more profound improvement in sleep quality and decreased postoperative pharyngeal discomfort, modified UPPP was an effective surgical method for the treatment of obstructive sleep apnea-hypopnea syndrome.