Patients with odontogenic cysts and tumors may have to undergo serious surgery unless the lesion is properly detected at the early stage. The purpose of this study is to evaluate the diagnostic performance of the real-time object detecting deep convolutional neural network You Only Look Once (YOLO) v2—a deep learning algorithm that can both detect and classify an object at the same time—on panoramic radiographs. In this study, 1602 lesions on panoramic radiographs taken from 2010 to 2019 at Yonsei University Dental Hospital were selected as a database. Images were classified and labeled into four categories: dentigerous cysts, odontogenic keratocyst, ameloblastoma, and no lesion. Comparative analysis among three groups (YOLO, oral and maxillofacial surgeons, and general practitioners) was done in terms of precision, recall, accuracy, and F1 score. While YOLO ranked highest among the three groups (precision = 0.707, recall = 0.680), the performance differences between the machine and clinicians were statistically insignificant. The results of this study indicate the usefulness of auto-detecting convolutional networks in certain pathology detection and thus morbidity prevention in the field of oral and maxillofacial surgery.

Objectives:The aim of this study was to identify variables that can be used for osteoporosis detection using strut analysis, fractal dimension (FD) and the gray level co-occurrence matrix (GLCM) using multiple regions of interest and to develop an osteoporosis detection model based on panoramic radiography.Methods:A total of 454 panoramic radiographs from oral examinations in our dental hospital from 2012 to 2015 were randomly selected, equally distributed among osteoporotic and non-osteoporotic patients (n = 227 in each group). The radiographs were classified by bone mineral density (T-score). After 3 marrow regions and the endosteal margin area were selected, strut features, FD and GLCM were analysed using a customized image processing program. Image upsampling was used to obtain the optimal binarization for calculating strut features and FD. The independent-samples t-test was used to assess statistical differences between the 2 groups. A decision tree and support vector machine were used to create and ...

This study aimed to develop an artificial intelligence model that can detect mesiodens on panoramic radiographs of various dentition groups. Panoramic radiographs of 612 patients were used for training. A convolutional neural network (CNN) model based on YOLOv3 for detecting mesiodens was developed. The model performance according to three dentition groups (primary, mixed, and permanent dentition) was evaluated, both internally (130 images) and externally (118 images), using a multi-center dataset. To investigate the effect of image preprocessing, contrast-limited histogram equalization (CLAHE) was applied to the original images. The accuracy of the internal test dataset was 96.2% and that of the external test dataset was 89.8% in the original images. For the primary, mixed, and permanent dentition, the accuracy of the internal test dataset was 96.7%, 97.5%, and 93.3%, respectively, and the accuracy of the external test dataset was 86.7%, 95.3%, and 86.7%, respectively. The CLAHE images yielded less accurate results than the original images in both test datasets. The proposed model showed good performance in the internal and external test datasets and had the potential for clinical use to detect mesiodens on panoramic radiographs of all dentition types. The CLAHE preprocessing had a negligible effect on model performance.

Objectives: To evaluate changes in the volume and cross-sectional area of the nasal airway before and 1 year after nonsurgical miniscrew-assisted rapid maxillary expansion (MARME) in young adults. Materials and Methods: Fourteen patients (mean age, 22.7 years; 10 women, four men) with a transverse discrepancy who underwent cone beam computed tomography before (T0), immediately after (T1), and 1 year after (T2) expansion were retrospectively included in this study. The volume of the nasal cavity and nasopharynx and the cross-sectional area of the anterior, middle, and posterior segments of the nasal airway were measured and compared among the three timepoints using paired t -tests. Results: The volume of the nasal cavity showed a significant increase at T1 and T2 ( P < .05), while that of the nasopharynx increased only at T2 ( P < .05). The anterior and middle cross-sectional areas significantly increased at T1 and T2 ( P < .05), while the posterior cross-sectional area showed no significant change throughout the observation period ( P > .05). Conclusions: The results demonstrate that the volume and cross-sectional area of the nasal cavity increased after MARME and were maintained at 1 year after expansion. Therefore, MARME may be helpful in expanding the nasal airway.

Jeong HG, et al. Imaging Sci Dent. 2017 Mar;47(1):45-50. https://doi.org/10.5624/isd.2017.47.1.45