The purpose of this study was to investigate the accuracy of the airway volume measurement by a Regression Neural Network-based deep-learning model. A set of manually outlined airway data was set to build the algorithm for fully automatic segmentation of a deep learning process. Manual landmarks of the airway were determined by one examiner using a mid-sagittal plane of cone-beam computed tomography (CBCT) images of 315 patients. Clinical dataset-based training with data augmentation was conducted. Based on the annotated landmarks, the airway passage was measured and segmented. The accuracy of our model was confirmed by measuring the following between the examiner and the program: (1) a difference in volume of nasopharynx, oropharynx, and hypopharynx, and (2) the Euclidean distance. For the agreement analysis, 61 samples were extracted and compared. The correlation test showed a range of good to excellent reliability. A difference between volumes were analyzed using regression analysis. The slope of the two measurements was close to 1 and showed a linear regression correlation (r2 = 0.975, slope = 1.02, p < 0.001). These results indicate that fully automatic segmentation of the airway is possible by training via deep learning of artificial intelligence. Additionally, a high correlation between manual data and deep learning data was estimated.

Objectives The purpose of this study was to evaluate risk factors and symptoms in cemento-osseous dysplasia (COD) patients. Materials and Methods In this study, 62 patients who were diagnosed histologically with COD were investigated from 2010 to 2020 at the author’s institution. We compared clinical and radiological characteristics of symptomatic and asymptomatic patients. The factors were sex, age, lesion size, site, radiologic stage of lesion, apical involvement, sign of infection, and history of tooth extraction. Statistical analysis was performed using Fisher’s exact test and the chi-square test. Results COD was more prevalent in female patients. With the exception of three cases, all were focal COD. The majority of patients presented with symptoms when the lesion was smaller than 1.5 cm in size. Symptoms were observed when the apex of the tooth was included in the lesion or there was a local infection around the lesion. The history of tooth extraction and previous endodontic treatment were evaluated, and history was not a significant predictor for the onset of symptoms. Conclusion In this study, risk factors associated with symptomatic patients were size of lesion, apical involvement, and local infection.

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Objectives: The purpose of this study is to investigate the characteristics of dentigerous and radicular cysts that occur between deciduous and succeeding permanent teeth and to propose considerations for differential diagnosis of cysts at the treatment planning stage in the outpatient clinic. Materials and Methods: A total of 87 patients with a cystic lesion located between a deciduous tooth and the succeeding permanent tooth participated in the study. Twelve variables were analyzed to diagnose such a cyst. For data analysis, Fisher's exact test was used to determine the statistical significance of the variables. Results: Of the total 87 patients who participated in this study, 69 were diagnosed with dentigerous cysts and 18 were diagnosed with radicular cysts. Seven of the 12 differential factors analyzed in this study were statistically significant: age, location, symptoms, dental caries, endodontic treatment, delayed eruption, and size. Conclusion: Several criteria can be considered for diagnosis of dentigerous cysts or radicular cysts. Age, location, presence of symptoms and dental caries, previous endodontic treatment, cystic size, and delayed eruption of impacted permanent teeth are reliable factors that should be considered when diagnosing dentigerous and radicular cysts.

(1) Background: The accurate diagnosis of periodontal disease typically involves complex clinical and radiologic examination. However, recent studies have demonstrated the potential of deep learning in improving diagnostic accuracy and reliability through the development of computer-aided detection and diagnosis algorithms for dental problems using various radiographic sources. This study focuses on the use of panoramic radiographs, which are preferred due to their ability to assess the entire dentition with a single radiation dose. The objective is to evaluate whether panoramic radiographs are a reliable source for the detection of periodontal bone loss using deep learning, and to assess its potential for practical use on a large dataset. (2) Methods: A total of 4083 anonymized digital panoramic radiographs were collected using a Proline XC machine (Planmeca Co., Helsinki, Finland) in accordance with the research ethics protocol. These images were used to train the Faster R-CNN object detection method for detecting periodontally compromised teeth on panoramic radiographs. (3) Results: This study demonstrated a high level of consistency and reproducibility among examiners, with overall inter- and intra-examiner correlation coefficient (ICC) values of 0.94. The Area Under the Curve (AUC) for detecting periodontally compromised and healthy teeth was 0.88 each, and the overall AUC for the entire jaw, including edentulous regions, was 0.91. (4) Conclusions: The regional grouping of teeth exhibited reliable detection performance for periodontal bone loss using a large dataset, indicating the possibility of automating the diagnosis of periodontitis using panoramic radiographs.