Purpose It has been proposed that using new prediction methods, such as neural networks based on dental data, could improve age estimation. This study aimed to assess the possibility of exploiting neural networks for estimating age by means of the pulp-to-tooth ratio in canines as a non-destructive, non-expensive, and accurate method. In addition, the predictive performance of neural networks was compared with that of a linear regression model. Materials and Methods Three hundred subjects whose age ranged from 14 to 60 years and were well distributed among various age groups were included in the study. Two statistical software programs, SPSS 21 (IBM Corp., Armonk, NY, USA) and R, were used for statistical analyses. Results The results indicated that the neural network model generally performed better than the regression model for estimation of age with pulp-to-tooth ratio data. The prediction errors of the developed neural network model were acceptable, with a root mean square error (RMSE) of 4.40 years and a mean absolute error (MAE) of 4.12 years for the unseen dataset. The prediction errors of the regression model were higher than those of the neural network, with an RMSE of 10.26 years and a MAE of 8.17 years for the test dataset. Conclusion The neural network method showed relatively acceptable performance, with an MAE of 4.12 years. The application of neural networks creates new opportunities to obtain more accurate estimations of age in forensic research.

In maxillofacial imaging, cone beam computed tomography (CBCT) is currently the modality of choice for assessment of bony structures of the temporomandibular joint (TMJ). Factors affecting the quality of CBCT images can change its diagnostic accuracy. This study aimed to assess the effect of field of view (FOV) and defect size on the accuracy of CBCT scans for detection of bone defects of the TMJs. This study was conducted on 12 sound TMJs of 6 human dry skulls. Erosions and osteophytes were artificially induced in 0.5, 1, and 1.5-mm sizes on the anterior-superior part of the condyle; CBCT scans were obtained with 6, 9, and 12-inch FOVs by NewTom 3G CBCT system. Two maxillofacial radiologists evaluated the presence/absence and type of defects on CBCT scans. The Cohen kappa was calculated to assess intra- and interobserver reliability. The Mann-Whitney U test was applied to compare the diagnostic accuracy of different FOVs.In comparison of 6- and 12-inch, 9- and 12-inch FOVs in detection of different sizes of erosive lesions, difference was significant (P <0.05), whereas difference between 6- and 9 inch just in 0.5-mm erosive lesion was significant (P = 0.04). In comparison of 6- and 12-inch FOVs in detection of different sizes of osteophyte lesion, difference was significant (P < 0.05), whereas between 6- and 9-inch FOVs statistically significant difference was not observed (P > 0.05). The highest and the lowest diagnostic accuracy of CBCT scans for condyle defects were obtained with 6-inch and 12-inch FOVs, respectively. Diagnostic accuracy of CBCT scans increased with an increase in size of bone defects.

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Objectives: Radiographic examination is one of the most important parts of the clinical assessment routine for temporomandibular disorders. The aim of this study was to compare the diagnostic accuracy of cone-beam computed tomography(CBCT) with panoramic radiography and spiral computed tomography for the detection of the simulated mandibular condyle bone lesions. Study Design: The sample consisted of 10 TMJs from 5 dried human skulls. Simulated erosive and osteophytic lesions were created in 3 different sizes using round diamond bur and bone chips, respectively. Panoramic radiography, spiral tomography and cone-beam computed tomography were used in defect detection. Data were statistically analyzed with the Mann-Whitney test. The reliability and degrees of agreement between two observers were also determined by the mean of Cohen’s Kappa analysis. Results: CBCT had a statistically significant superiority than other studied techniques in detection of both erosive and osteophytic lesions with different sizes. There were significant differences between tomography and panoramic in correct detection of both erosive and osteophytic lesions with 1mm and 1.5 mm in size. However, there were no significant differences between Tomography and Panoramic in correct detection of both erosive and osteophytic lesions with 0.5 mm in size. Conclusions: CBCT images provide a greater diagnostic accuracy than spiral tomography and panoramic radiography in the detection of condylar bone erosions and osteophytes. Key words:Bone defect, Condyle, CBCT, Panoramic, radiography.

Background This study aimed to estimate the chronological age of individuals according to the correlation of age with morphological variables of the maxillary canine teeth using cone-beam computed tomography (CBCT) based on Kvaal’s method. Method This study was conducted on CBCT scans of 300 patients in Hamadan city, including 142 females and 158 males between 14 and 60 years of age. To measure the morphological variables, cross-sectional views of the maxillary right canine tooth were studied. The pulp to tooth area ratio (AR), the pulp to tooth length ratio (P), the mesiodistal and the buccolingual pulp to tooth width ratio at the cementoenamel junction (CEJ) (A1, A2), the mesiodistal and buccolingual pulp to tooth width ratio at the mid-root (C1, C2), and the mesiodistal and buccolingual pulp to tooth width ratio at the midpoint between the CEJ and the mid-root (B1, B2) were measured on CBCT scans. Results A significant inverse correlation was noted between age and all measured variables. No significant difference was found in the mean variables between males and females. The correlation between the actual age and estimated age in the regression model was 0.88. The mean square error (MSE) of prediction was 5.89 years; also, the mean absolute error (MAE) was 4.46 years. Conclusion The fitted regression model suggested in this study can estimate the age of individuals with acceptable accuracy and mean absolute error of lower than 5 years.

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CBCT was only useful for detection of cavities located in the apical one-third of the root, compared to other digital or conventional methods.

Background: Beam hardening and scattering artifacts from high-density objects such as dental implants adversely affect the image quality and subsequently the detection of fenestration or dehiscence around dental implants. Objective: This study aimed to assess the efficacy of metal artifact reduction (MAR) algorithm of two cone-beam computed tomography (CBCT) systems for detection of peri-implant fenestration and dehiscence. Material and Methods: In this experimental study, thirty-six titanium implants were placed in bone blocks of bovine ribs. Fenestration and dehiscence were created in the buccal bone around implants. CBCT images were obtained using Cranex 3D and ProMax 3D CBCT systems with and without MAR algorithm. Two experienced radiologists observed the images. Data were analyzed using SPSS software. The Kappa coefficient of agreement, the area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy of different imaging modalities were calculated and analyzed. Results: In both CBCT systems, the use of MAR algorithm decreased the area under the ROC curve and subsequently the diagnostic accuracy for the detection of fenestration and dehiscence. The sensitivity, specificity and accuracy of both CBCT systems were higher in absence of the MAR algorithm. The specificity of ProMax 3D for detection of fenestration was equal with/without the MAR algorithm. Conclusion: Although CBCT is suitable for detection of peri-implant defects, the application of the MAR algorithm does not enhance the detection of peri-implant fenestration and dehiscence.