Background: The anatomical variation of the anterior superior alveolar nerve described as canalis sinuosus (CS) is (Folia Morphol 2018; 77, 3: 551-557)

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Objectives: The present study aimed to evaluate the performance of a Faster Region-based Convolutional Neural Network (R-CNN) algorithm for tooth detection and numbering on periapical images. Methods: The data sets of 1686 randomly selected periapical radiographs of patients were collected retrospectively. A pre-trained model (GoogLeNet Inception v3 CNN) was employed for pre-processing, and transfer learning techniques were applied for data set training. The algorithm consisted of: (1) the Jaw classification model, (2) Region detection models, and (3) the Final algorithm using all models. Finally, an analysis of the latest model has been integrated alongside the others. The sensitivity, precision, true-positive rate, and false-positive/negative rate were computed to analyze the performance of the algorithm using a confusion matrix. Results: An artificial intelligence algorithm (CranioCatch, Eskisehir-Turkey) was designed based on R-CNN inception architecture to automatically detect and number the teeth on periapical images. Of 864 teeth in 156 periapical radiographs, 668 were correctly numbered in the test data set. The F1 score, precision, and sensitivity were 0.8720, 0.7812, and 0.9867, respectively. Conclusion: The study demonstrated the potential accuracy and efficiency of the CNN algorithm for detecting and numbering teeth. The deep learning-based methods can help clinicians reduce workloads, improve dental records, and reduce turnaround time for urgent cases. This architecture might also contribute to forensic science.

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Background: Description of the nasopalatine canal (NPC) is important for planning surgical treatment, comprehension of the morphology and pathogenesis of lesions that occur in the anterior maxilla. The goal of this study was to analyze the dimensions and anatomic characteristics of the NPC on cone-beam computed tomography (CBCT) scans; to determine the incidence of anatomical variation; and to assess the correlations of these variables with age, gender, and dental status. Materials and methods: A total of 320 individual CBCT images were included. Reformatted sagittal, coronal and axial slices were evaluated. Sagittal images were used for measurements of the NPC and to classified shape and direction-course of the NPC. Coronal images were used to analyzed the NPC division levels and axial images were used to detect the number of palatal and nasal opening. Results: The mean NPC length was 11.45 ± 2.50 mm, statistically significant differences were detected between males and females (p < 0.05). Mean nasopalatine angle was 76.26° ± 8.12°, significant differences were detected in sagittal and coronal classifications. The most common canal was: funnel shapes (29%); slanted-curved direction-course (53.1%); middle 2 third division level (43.1%); and one incisive foramen with two Stenson's foramina (1-2) (77.2%). Conclusions: The current study ensures new findings on the literature concerning the description of the anatomical structure of the canal. Also, the study highlights a significant variability in the anatomy and morphology of the NPC. Therefore, three-dimensional analysis of this structure is important for facilitating surgical management and preventing possible complications in this area.