Background: The aim of this study was to evaluate the morphometric analysis of sella turcica in a Turkish population according to gender, age, and dentofacial skeletal type and to investigate the prevalence of sella turcica shapes in different dentofacial skeletal types. Materials and methods: The lateral cephalometric radiographs of 362 patients (145 males, 217 females) were included and grouped by age, gender, and dentofacial skeletal patterns. Linear dimensions of sella turcica, which include the length, height, and diameter, were measured, and the shapes of sella turcica were evaluated. Results: The anatomical variants of the sella turcica in this study were normal morphology (39.0%), followed by pyramidal shape (15.5%), double contour of floor (14.6%), oblique anterior wall (14.4%), irregular dorsum sella (8.6%), and sella turcica bridge (8.0%). Significant differences were found between sella turcica shapes and dentofacial skeletal types (p<0.01). Females had greater diameter size of sella turcica than males (p<0.01). In addition, the subjects in the 15–21 age group had larger sella turcica depths and diameters than the subjects in the 9–14 age group (p<0.05 andp0.05). Conclusions: Results from this study showed that the sample had a higher rate of morphological variation (39% normal, 61% other types) in comparison with other populations or ethnic groups. The Class III patients had more irregularity (notching) types in the posterior part of the dorsum sella and fewer oblique anterior wall types than the others. Linear dimensions and morphological types of sella turcica in this study can be used as reference for additional investigators, such as radiologists, orthodontists, maxillofacial surgeons, and neurosurgeons, to interpret and plan surgical procedures involving the sellar region. Background: The aim of this study was to evaluate the morphometric analysis of sella turcica in a Turkish population according to gender, age, and dentofacial skeletal type and to investigate the prevalence of sella turcica shapes in different dentofacial skeletal types. Materials and methods: The lateral cephalometric radiographs of 362 patients (145 males, 217 females) were included and grouped by age, gender, and dentofacial skeletal patterns. Linear dimensions of sella turcica, which include the length, height, and diameter, were measured, and the shapes of sella turcica were evaluated. Results: The anatomical variants of the sella turcica in this study were normal morphology (39.0%), followed by pyramidal shape (15.5%), double contour of floor (14.6%), oblique anterior wall (14.4%), irregular dorsum sella (8.6%), and sella turcica bridge (8.0%). Significant differences were found between sella turcica shapes and dentofacial skeletal types (p<0.01). Females had greater diameter size of sella turcica than males (p<0.01). In addition, the subjects in the 15–21 age group had larger sella turcica depths and diameters than the subjects in the 9–14 age group (p<0.05 andp0.05). Conclusions: Results from this study showed that the sample had a higher rate of morphological variation (39% normal, 61% other types) in comparison with other populations or ethnic groups. The Class III patients had more irregularity (notching) types in the posterior part of the dorsum sella and fewer oblique anterior wall types than the others. Linear dimensions and morphological types of sella turcica in this study can be used as reference for additional investigators, such as radiologists, orthodontists, maxillofacial surgeons, and neurosurgeons, to interpret and plan surgical procedures involving the sellar region.

Background: The aim of this study was to analyze as three dimensions the anatomical and morphometric dimensions of nasopalatine canal (NPC) in the coronal, axial and sagittal directions using cone beam volumetric tomography (CBCT) and to evaluate the effect of age, gender and maxillary dental status on NPC and buccal bone thickness (BBT). Methods: In this study, CBCT data of the 619 individuals aged between 17-86 years were examined retrospectively with respect to dimensions and anatomic variations of the NPC and BBTs. The correlation of age, gender, and status of edentulism of anterior maxilla with all the variables were evaluated. Results: The NPC and BBT showed important variability in terms of morphology and dimensions. Morphological assessment of NPC revealed that 26.17% of NPCs were conical shape, 24.71% of canals were hourglass-shaped, 16.80% of canals were cylindrical shaped, 15.83 % of canals were funnel-like shaped, 11.14 % of canals were banane-like shaped, and 5.33 % of canals were tree branch-like shaped. Males and females showed significant differences in the length of the NPC and BBTs in the sagittal sections. There were significant differences between age and BBTs. In addition, the length of NPC and BBTs were statistically different according to dental status. Conclusions: These anatomical changes in terms of dimensional and morphological parameters revealed the importance of 3D imaging. Dentists should know and consider the variations in this canal in order to avoid possible complications during anesthesia and surgical procedures which were applied to the maxillary anterior region. More precautions should be taken during surgical procedures in females, elderly and edentulous patients. Background: The aim of this study was to analyze as three dimensions the anatomical and morphometric dimensions of nasopalatine canal (NPC) in the coronal, axial and sagittal directions using cone beam volumetric tomography (CBCT) and to evaluate the effect of age, gender and maxillary dental status on NPC and buccal bone thickness (BBT). Methods: In this study, CBCT data of the 619 individuals aged between 17-86 years were examined retrospectively with respect to dimensions and anatomic variations of the NPC and BBTs. The correlation of age, gender, and status of edentulism of anterior maxilla with all the variables were evaluated. Results: The NPC and BBT showed important variability in terms of morphology and dimensions. Morphological assessment of NPC revealed that 26.17% of NPCs were conical shape, 24.71% of canals were hourglass-shaped, 16.80% of canals were cylindrical shaped, 15.83 % of canals were funnel-like shaped, 11.14 % of canals were banane-like shaped, and 5.33 % of canals were tree branch-like shaped. Males and females showed significant differences in the length of the NPC and BBTs in the sagittal sections. There were significant differences between age and BBTs. In addition, the length of NPC and BBTs were statistically different according to dental status. Conclusions: These anatomical changes in terms of dimensional and morphological parameters revealed the importance of 3D imaging. Dentists should know and consider the variations in this canal in order to avoid possible complications during anesthesia and surgical procedures which were applied to the maxillary anterior region. More precautions should be taken during surgical procedures in females, elderly and edentulous patients.

Objectives: This study aimed to assess the effectiveness of deep convolutional neural network (CNN) algorithms for the detecting and segmentation of overhanging dental restorations in bitewing radiographs. Methods: A total of 1160 anonymized bitewing radiographs were used to progress the artificial intelligence (AI) system for the detection and segmentation of overhanging restorations. The data were then divided into three groups: 80% for training (930 images, 2399 labels), 10% for validation (115 images, 273 labels), and 10% for testing (115 images, 306 labels). A CNN model known as You Only Look Once (YOLOv5) was trained to detect overhanging restorations in bitewing radiographs. After utilizing the remaining 115 radiographs to evaluate the efficacy of the proposed CNN model, the accuracy, sensitivity, precision, F1 score, and area under the receiver operating characteristic curve (AUC) were computed. Results: The model demonstrated a precision of 90.9%, a sensitivity of 85.3%, and an F1 score of 88.0%. Furthermore, the model achieved an AUC of 0.859 on the receiver operating characteristic (ROC) curve. The mean average precision (mAP) at an intersection over a union (IoU) threshold of 0.5 was notably high at 0.87. Conclusions: The findings suggest that deep CNN algorithms are highly effective in the detection and diagnosis of overhanging dental restorations in bitewing radiographs. The high levels of precision, sensitivity, and F1 score, along with the significant AUC and mAP values, underscore the potential of these advanced deep learning techniques in revolutionizing dental diagnostic procedures.

Guldane Magat, Elif Oncu, Sevgi Ozcan, Kaan Orhan. JKAOMS 2022;48:41-9. https://doi.org/10.5125/jkaoms.2022.48.1.41

Magat G, et al. Imaging Sci Dent. 2022 Feb;52:e7. https://doi.org/10.5624/isd.20210231