Purpose: This study aimed to develop a deep learning model incorporating the pulp-to-tooth area ratio (PTR) for estimating age from periapical radiographs of upper central incisors in a Thai population. Materials and Methods: A total of 2,041 periapical radiographs, representing 3,108 upper central incisors from individuals aged 10.00 to 84.25 years, were analyzed. Root and root canal segmentation masks were generated using Labelbox. The dataset was randomly divided into training (2,175 teeth), validation (466 teeth), and test (467 teeth) sets.Model development proceeded in 3 steps: (1) DeepLabv3 + was trained to segment root and root canal regions. (2) ResNet-50 was trained to estimate age using both segmented images and PTR values derived from the masks. (3) The validation set guided model selection, and the final model was evaluated on the test set using mean absolute error (MAE). Results: The segmentation model (DeepLabv3 + ) achieved a mean intersection over union (mIoU) of 81.85%. The age estimation model (ResNet-50) yielded an overall MAE of 6.14 years. For age-specific evaluation, test data were grouped into 4 categories: 10-19 years, 20-39 years, 40-59 years, and ≥60 years. The MAE was lowest in the 10-19 years group (3.74 years) and progressively increased with age: 6.07, 6.58, and 8.40 years, respectively. Conclusion This study demonstrated that integrating deep learning models with PTR measurement offers a promising method for dental radiographic age estimation using a single tooth.

Purpose: The objective of this study was to evaluate and compare the accuracy and image quality of root surface area (RSA) measurements obtained with various cone-beam computed tomography (CBCT) protocols, relative to the gold standard of micro-computed tomography (CT), in an in vitro setting. Materials and Methods: Four dry human skulls were scanned using 8 different protocols, with voxel sizes of 0.15 mm, 0.3 mm, and 0.4 mm. Three-dimensional models of the selected teeth were constructed using CBCT and microCT protocols, and the RSA was automatically measured by the image-processing software. The absolute difference in the percentage of the RSA (%ΔRSA) was calculated and compared across the 8 CBCT protocols using repeatedmeasures analysis of variance. Finally, image quality scores of the RSA measurements were computed and reported in terms of percent distribution. Results: No significant differences were observed in the %ΔRSA across the 8 protocols (P>0.05). The deviation in %ΔRSA ranged from 1.51% to 4.30%, with an increase corresponding to voxel size. As the voxel size increased, the image quality deteriorated. This decline in quality was particularly noticeable at the apical level of the root, where the distribution of poorer scores was most concentrated. Conclusion Relative to CBCT protocols with voxel sizes of 0.15 mm and 0.3 mm, the protocols with a voxel size of 0.4 mm demonstrated inferior image quality at the apical levels. In spite of this, no significant discrepancies were observed in RSA measurements across the different CBCT protocols.

PURPOSE: This study assessed the accuracy of age estimates produced by a regression equation derived from lower third molar development in a Thai population. MATERIALS AND METHODS: The first part of this study relied on measurements taken from panoramic radiographs of 614 Thai patients aged from 9 to 20. The stage of lower left and right third molar development was observed in each radiograph and a modified Gat score was assigned. Linear regression on this data produced the following equation: Y=9.309+1.673 mG+0.303S (Y=age; mG=modified Gat score; S=sex). In the second part of this study, the predictive accuracy of this equation was evaluated using data from a second set of panoramic radiographs (539 Thai subjects, 9 to 24 years old). Each subject's age was estimated using the above equation and compared against age calculated from a provided date of birth. Estimated and known age data were analyzed using the Pearson correlation coefficient and descriptive statistics. RESULTS: Ages estimated from lower left and lower right third molar development stage were significantly correlated with the known ages (r=0.818, 0.808, respectively, P≤0.01). 50% of age estimates in the second part of the study fell within a range of error of ±1 year, while 75% fell within a range of error of ±2 years. The study found that the equation tends to estimate age accurately when individuals are 9 to 20 years of age. CONCLUSION: The equation can be used for age estimation for Thai populations when the individuals are 9 to 20 years of age.
Age Determination by Teeth ; Asian Continental Ancestry Group* ; Forensic Dentistry ; Humans ; Linear Models ; Molar, Third* ; Parturition ; Radiography, Panoramic

PURPOSE: The aim of this study was to compare the diagnostic accuracy of cone-beam computed tomography (CBCT) images and bitewing images in detection of secondary caries. MATERIALS AND METHODS: One hundred and twenty proximal slots of Class II cavities were randomly prepared on human premolar and molar teeth, and restored with amalgam (n=60) and composite resin (n=60). Then, artificial secondary caries lesions were randomly created using round steel No. 4 bur. The teeth were radiographed with a conventional bitewing technique and two CBCT systems; Pax-500ECT and Promax 3D. All images were evaluated by five observers. The area under the receiver operating characteristic (ROC) curve (Az) was used to evaluate the diagnostic accuracy. Significant difference was tested using the Friedman test (p value<0.05). RESULTS: The mean Az values for bitewing, Pax-500ECT, and Promax 3D imaging systems were 0.882, 0.995, and 0.978, respectively. Significant differences were found between the two CBCT systems and film (p=0.007). For CBCT systems, the axial plane showed the greatest Az value. CONCLUSION: Based on the design of this study, CBCT images were better than bitewing radiographs in detection of secondary caries.
Bicuspid ; Cone-Beam Computed Tomography ; Dental Caries ; Humans ; Molar ; Radiography, Bitewing ; ROC Curve ; Steel ; Tooth