We observed that as the anterior sagittal angle gets wider, SHC tends to get longer. The most observed morphological variations were linear shape, elongated type and calcified outline pattern. Detailed studies on the classification will contribute to the literature. (Folia Morphol 2018; 77, 1: 79-89).

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The study aimed to generate a fused deep learning algorithm that detects and classifies the relationship between the mandibular third molar and mandibular canal on orthopantomographs. Radiographs (n = 1880) were randomly selected from the hospital archive. Two dentomaxillofacial radiologists annotated the data via MATLAB and classified them into four groups according to the overlap of the root of the mandibular third molar and mandibular canal. Each radiograph was segmented using a U-Net-like architecture. The segmented images were classified by AlexNet. Accuracy, the weighted intersection over union score, the dice coefficient, specificity, sensitivity, and area under curve metrics were used to quantify the performance of the models. Also, three dental practitioners were asked to classify the same test data, their success rate was assessed using the Intraclass Correlation Coefficient. The segmentation network achieved a global accuracy of 0.99 and a weighted intersection over union score of 0.98, average dice score overall images was 0.91. The classification network achieved an accuracy of 0.80, per class sensitivity of 0.74, 0.83, 0.86, 0.67, per class specificity of 0.92, 0.95, 0.88, 0.96 and AUC score of 0.85. The most successful dental practitioner achieved a success rate of 0.79. The fused segmentation and classification networks produced encouraging results. The final model achieved almost the same classification performance as dental practitioners. Better diagnostic accuracy of the combined artificial intelligence tools may help to improve the prediction of the risk factors, especially for recognizing such anatomical variations.

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SUMMARY:The aim of this study was to assess the frequency of the BMC phenomenon in a Turkish patient population. Cone beam computed tomography (CBCT) images of 2634 consecutive patients were retrospectively reviewed. The Chi-squared test was used to determine potential differences in the distribution of BMCs when stratified by sex and side. Among the 2634 patients, 42 (1.7%) patients were found to have BMC. Of these 42 patients, 22 were female (0.8%) and 20 were male (0.7%) with age ranging from 29 to 68 years (mean age 47.47). Among the 42 patients, 39 (92.8%) of the BMCs were unilateral and three (7.1%) were bilateral. Approximately 24 cases (53.3%) were on the right side, and 21 cases (46.6%) were on the left side. All of the BMCs showed a mediolateral orientation. The mean depth of the BMC was 2.55 mm in males and 2.68 mm in females. 2 patients have symptoms whereas the other patients were atraumatic and asymptomatic. BMC is a rare condition that might be more prevalent in the Turkish population. Greater detailed information regarding BMC could be obtained by the widespread use of CBCT in epidemiological studies.