Background Successful dental root canal treatments require a complete knowledge of dental anatomy and root canal morphology. Materials and Methods One hundred and forty-five cone beam computed tomography (CBCT) images were used to assess the anatomy and morphology of mandibular premolars based on Vertucci's classifications in a defined group of dental patients in Iran. The number of roots and root canals, root canal morphology, root and canal shape (curvature), existence of C-shaped canal, and influence of sex on each of these were evaluated. A chi-squared test was used for statistical analysis. Results The mandibular first and second premolars had a single root in 95.97% and 100% cases, respectively. In the mandibular first premolars, 62.2% were of type I, 0.8% type II, 10.9% type III, 0.8% type IV, 20.3% type V, 4.2% type VI, and 0.8% type VII; in the second premolars, 78% of canals were of type I, 3% type II, 11% type III, 7% type V, and 1% type VI. C-shaped canals did not exist in either of the premolars. The most prevalent root and canal shape was straight. The most prevalent root curvature was a distal curvature in both premolars (71.4% and 74% of first and second premolars, resp.). The most prevalent canal curvature was lingual and buccal for the first premolars (7.6% each) and distal for the second premolars (11%). No significant difference was found between men and women in nearly all of the above (P > 0.05). Conclusion The results suggest that there is a need to conduct further evaluations on finding root and canal variations among more populations to gain better knowledge prior to root canal treatment.

Root fracture diagnosis is a clinical difficulty that in most cases can only be detected through radiography. The objective of this study was to compare the diagnostic accuracy of two types of digitally enhanced images (reverse-contrast and colorization) with original digital radiographies in detecting experimental root fractures. Two hundred extracted single-rooted human teeth were endodontically instrumented and then divided in two groups, one control group and one test group, including fractured teeth. Vertical root fractures were experimentally made in the fractured group. The digital image of each tooth was taken, using the paralleling technique. There were three groups of images: (i) original, (ii) reverse contrast, and (iii) colorized. Three experienced dental specialists examined the images with no prior knowledge of the distribution of the root fractures. Two-way analysis of variance was used to assess the differences in accuracy, sensitivity, and specificity of each technique in detecting root fractures. Cohen's kappa coefficients were calculated to investigate the degree of interobserver agreement. The accuracy, sensitivity, and specificity of original images were 67.4%, 66.7%, and 68%, respectively; these amounts were 61.5%, 61%, and 65.5% in reverse contrast images and 66.4%, 70.7%, and 62% in colorized digital radiography. The original images had the best inter observer kappa coefficients (between 0.45 and 0.55). The results showed that the accuracy of original images is better than reverse contrast and colorized images. Use of reverse-contrast and colorization digital images in root fracture detection should be regarded as an adjunct to other diagnostic methods not as a highly critical diagnostic aid.

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No significant difference was seen between the two techniques. The specificity of DDR was slightly better than CR, and their accuracy and sensitivity showed small differences.

Objectives. This study aimed to assess the radiographic position of impacted mandibular third molars (IMTMs) and their association with pathological conditions. Materials and Methods. The impaction depth, relationship with ramus, and angulation of 1600 IMTMs and their association with 2nd molar distal caries and root resorption, pathological conditions, and proximity to the mandibular canal were evaluated on panoramic radiographs. The IMTM position was determined based on the depth of impaction according to the Pell and Gregory classification, relationship with ramus according to the Pell and Gregory classification, and angulation according to the Winter’s classification. The classical and Bayesian logistic regressions were applied to analyze the effect of IMTM position on the associated complications using the odds ratio (OR) and 95% confidence interval (credible interval for Bayesian models). Two-tailed P value < 0.05 was considered statistically significant. Results. Of 1600 IMTMs evaluated in this study, 195 (12.2%), 252 (15.8%), and 119 (7.4%) had caused second molar distal caries, second molar root resorption, and pathological lesions, respectively, and 872 (54.5%) had contact with the mandibular canal. Impaction angulation was a risk factor for second molar distal caries (maximum OR = 5.01, 95% CI: 3.12–8.18). Changed angulation and greater impaction depth were the risk factors for second molar root resorption (minimum OR = 1.64, 95% CI: 0.58–4.02). Decreased distance between the ramus and distal side of the second molar was a risk factor for associated pathological lesions (minimum OR = 2.73, 95% CI: 1.79–4.25). Mesioangular and horizontal angulations and greater impaction depth were the risk factors for contact with the mandibular canal (maximum OR = 3.44, 95% CI: 2.6–4.57 and minimum OR = 1.3, 95% CI: 094–1.8). Conclusions. The frequency of complications associated with IMTMs was low, but considerable. The occurrence of these conditions might be affected by the impaction position. Thus, regular follow-ups are recommended in order to be able to surgically intervene when the first signs of pathologies arise.

BackgroundThe present study was performed with the following aims: (1) to assess the relationship between skeletal age, measured using the cervical vertebral maturity (CVM) method, and chronological age; (2) to determine the correlation of skeletal and chronological age to the cortical thickness of the lower border of the mandible using the linear radiomorphometric; and (3) to explore the relationship between these indices and skeletal malocclusion type.Material and MethodsThe data were collected from the records of 180 patients, including 57 males (31.7%) and 123 females (68.3%). The data were based on the panoramic and lateral cephalograms of each patient. The CVM stages were determined on the basis of the patients’ lateral cephalograms. Three radiomorphometric indices were measured: AI, MI and GI. The patients were divided up into three groups of skeletal malocclusion: Class I, II, and III. For all the tests, statistical significance was set at P<0.05.ResultsThe relationship between chronological age and skeletal age was 0.496. Furthermore, with an increase in chronological and skeletal age, the cortical thickness of the lower border of the mandible and consequently the radiomorphometric indices increase, except for the GI (P > 0.05). Lastly, the relationship between GI and skeletal malocclusion type proved significant.ConclusionsAI and MI were found to increase significantly with increasing age, so the assessment of mandibular radiomorphometric indices could be clinically useful in estimating of the growth and maturation of the mandible. Key words:Orthodontics, Radiomorphometric indices, Skeletal age, Skeletal malocclusion.

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Early diagnosis of oral cancer is critical to improve the survival rate of patients. The current strategies for screening of patients for oral premalignant and malignant lesions unfortunately miss a significant number of involved patients. Optical coherence tomography (OCT) is an optical imaging modality that has been widely investigated in the field of oncology for identification of cancerous entities. Since the interpretation of OCT images requires professional training and OCT images contain information that cannot be inferred visually, artificial intelligence (AI) with trained algorithms has the ability to quantify visually undetectable variations, thus overcoming the barriers that have postponed the involvement of OCT in the process of screening of oral neoplastic lesions. This literature review aimed to highlight the features of precancerous and cancerous oral lesions on OCT images and specify how AI can assist in screening and diagnosis of such pathologies.