Background:In orthodontic science, diagnosis of facial skeletal type (class I, II, and III) is essential to make the correct treatment plan that is usually expensive and complicated. Sometimes results from analysis of lateral cephalometry radiographies are not enough to discriminate facial skeletal types. In this situation, knowledge about the relationship between the shape and size of the sella turcica and the type of facial skeletal class can help to make a more definitive decision for treatment plan.Objectives:The present study was designed to investigate this relationship in patients referred to a dental school in Iran.Patients and Methods:In this descriptive-analytical study, cephalometric radiographies of 90 candidates for orthodontic treatment (44 females and 46 males) with an age range of 14 - 26 years and equal distribution in terms of class I, class II, and class III facial skeletal classification were selected. The shape, length, diameter, and depth of the sella turcica were determined on the radiographs. Linear dimensions were assessed by one-way analysis of variance while the correlation between the dimensions and age was investigated using Pearson’s correlation coefficient.Results:Sella turcica had normal morphology in 24.4% of the patients while irregularity (notching) in the posterior part of the dorsum sella was observed in 15.6%, double contour of sellar floor in 5.6%, sella turcica bridge in 23.3%, oblique anterior wall in 20% and pyramidal shape of the dorsum sella in 11.1% of the subjects. In total, 46.7% of class I patients had a normal shape of sella turcica, 23.3% of class II patients had an oblique anterior wall and a pyramidal shape of the dorsum sella, and 43.3% of class III individuals had sella turcica bridge (the greatest values). Sella turcica length was significantly greater in class III patients compared to class II and class I (P < 0.0001). However, depth and diameter of sella turcica were similar in class I, class II, and class III patients. Furthermore, age was significantly correlated to the diameter of sella turcica as greater diameters were observed in older ages (P < 0.04).Conclusion:A significant relationship exists between the type of facial skeletal classification and the shape of the sella turcica; as in class III patients, sella turcica bridge was reported with a higher frequency. Also, sella turcica had a significantly higher length in these patients than in those with class I and class II facial skeletal types.

Our results showed high frequencies of idiopathic osteosclerosis in Iran in comparison to some other countries.

Background:In orthodontic science, diagnosis of facial skeletal type (class I, II, and III) is essential to make the correct treatment plan that is usually expensive and complicated. Sometimes results from analysis of lateral cephalometry radiographies are not enough to discriminate facial skeletal types. In this situation, knowledge about the relationship between the shape and size of the sella turcica and the type of facial skeletal class can help to make a more definitive decision for treatment plan. Objectives: The present study was designed to investigate this relationship in patients referred to a dental school in Iran. Patients and Methods:In this descriptive-analytical study, cephalometric radiographies of 90 candidates for orthodontic treatment (44 females and 46 males) with an age range of 14 -26 years and equal distribution in terms of class I, class II, and class III facial skeletal classification were selected. The shape, length, diameter, and depth of the sella turcica were determined on the radiographs. Linear dimensions were assessed by one-way analysis of variance while the correlation between the dimensions and age was investigated using Pearson's correlation coefficient. Results: Sella turcica had normal morphology in 24.4% of the patients while irregularity (notching) in the posterior part of the dorsum sella was observed in 15.6%, double contour of sellar floor in 5.6%, sella turcica bridge in 23.3%, oblique anterior wall in 20% and pyramidal shape of the dorsum sella in 11.1% of the subjects. In total, 46.7% of class I patients had a normal shape of sella turcica, 23.3% of class II patients had an oblique anterior wall and a pyramidal shape of the dorsum sella, and 43.3% of class III individuals had sella turcica bridge (the greatest values). Sella turcica length was significantly greater in class III patients compared to class II and class I (P < 0.0001). However, depth and diameter of sella turcica were similar in class I, class II, and class III patients. Furthermore, age was significantly correlated to the diameter of sella turcica as greater diameters were observed in older ages (P < 0.04). Conclusion: A significant relationship exists between the type of facial skeletal classification and the shape of the sella turcica; as in class III patients, sella turcica bridge was reported with a higher frequency. Also, sella turcica had a significantly higher length in these patients than in those with class I and class II facial skeletal types.

Background:Radiographs, adjunct to clinical examination are always valuable complementary methods for dental caries detection. Recently, progressing in digital imaging system provides possibility of software designing for automatically dental caries detection.Objectives:The aim of this study was to develop and assess the function of diagnostic computer software designed for evaluation of approximal caries in posterior teeth. This software should be able to indicate the depth and location of caries on digital radiographic images.Materials and Methods:Digital radiographs were obtained of 93 teeth including 183 proximal surfaces. These images were used as a database for designing the software and training the software designer. In the design phase, considering the summed density of pixels in rows and columns of the images, the teeth were separated from each other and the unnecessary regions; for example, the root area in the alveolar bone was eliminated. Therefore, based on summed intensities, each image was segmented such that each segment contained only one tooth. Subsequently, based on the fuzzy logic, a well-known data-clustering algorithm named fuzzy c-means (FCM) was applied to the images to cluster or segment each tooth. This algorithm is referred to as a soft clustering method, which assigns data elements to one or more clusters with a specific membership function. Using the extracted clusters, the tooth border was determined and assessed for cavity. The results of histological analysis were used as the gold standard for comparison with the results obtained from the software. Depth of caries was measured, and finally Intraclass Correlation Coefficient (ICC) and Bland-Altman plot were used to show the agreement between the methods.Results:The software diagnosed 60% of enamel caries. The ICC (for detection of enamel caries) between the computer software and histological analysis results was determined as 0.609 (95% confidence interval [CI] = 0.159-0.849) (P = 0.006). Also, the computer program diagnosed 97% of dentin caries and the ICC between the software and histological analysis results for dentin caries was determined as 0.937 (95% CI=0.906-0.958) (P < 0.001). Bland-Altman plot showed an acceptable agreement for measuring the depth of caries in enamel and dentin.Conclusions:The designed software was able to detect a significant number of dentin caries and acceptable measuring of the depth of carious lesions in enamel and dentin. However, the software had limited ability in detecting enamel lesions.

No abstract