This study aimed to predict the skeletal growth maturation using convolutional neural network-based deep learning methods using cervical vertebral maturation and the lower 2nd molar calcification level so that skeletal maturation can be detected from orthopantomography using multiclass classification. About 1200 cephalometric radiographs and 1200 OPGs were selected from patients seeking treatment in dental centers. The level of skeletal maturation was detected by CNN using the multiclass classification method, and each image was identified as a cervical vertebral maturation index (CVMI); meanwhile, the chronological age was estimated from the level of the 2nd molar calcification. The model’s final result demonstrates a high degree of accuracy with which each stage and gender can be predicted. Cervical vertebral maturation reported high accuracy in males (98%), while females showed high accuracy of 2nd molar calcification. CNN multiclass classification is an accurate method to detect the level of maturation, whether from cervical maturation or the calcification of the lower 2nd molar, and the calcification level of the lower 2nd molar is a reliable method to trust in the growth level, so the traditional OPG is enough for this purpose.

This study aims to examine the impact of the direct and indirect mixing techniques on the dispersion and homogeneity of magnesium oxide (MgO) and silver (Ag) nanoparticles (NPs) mixed with polymethylmethacrylate (PMMA). NPs were mixed with PMMA powder directly (non-ethanol-assisted) and indirectly (ethanol-assisted) with the aid of ethanol as solvent. X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), and scanning electron microscope (SEM) were used to evaluate the dispersion and homogeneity of MgO and Ag NPs within the PMMA-NPs nanocomposite matrix. Prepared discs of PMMA-MgO and PMMA-Ag nanocomposite were analyzed for dispersion and agglomeration by Stereo microscope. XRD showed that the average crystallite size of NPs within PMMA-NP nanocomposite powder was smaller in the case of ethanol-assisted mixing compared to non-ethanol-assisted mixing. Furthermore, EDX and SEM revealed good dispersion and homogeneity of both NPs on PMMA particles with ethanol-assisted mixing compared to the non-ethanol-assisted one. Again, the PMMA-MgO and PMMA-Ag nanocomposite discs were found to have better dispersion and no agglomeration with ethanol-assisted mixing when compared to the non-ethanol-assisted mixing technique. Ethanol-assisted mixing of MgO and Ag NPs with PMMA powder obtained better dispersion, better homogeneity, and no agglomeration of NPs within the PMMA-NP matrix.

Hard palate is regarded as an important part of the human skull, which contributes to the separation of the oral and nasal cavities. The aims of the study were to investigate the morphology of the hard palate in order to create a general guideline of three-dimensional values of the palate in a Kurdish sample in the city of Sulaimani as well as determining the possible correlations between different palatal parameters in class I malocclusion with the maxillary arch form and perimeter. A retrospective study design was adopted by collecting 100 study models of orthodontic patients aged 16–24 years old attending different private dental clinics in the city of Sulaimani seeking orthodontic management. In this study, three-dimensional palatal measurements including depth, length, and width were measured in an attempt to discover their correlation with each maxillary arch form and perimeter. Additionally, measurements of inter-molar width, inter-canine width, and arch perimeter were carried out. About two-thirds of those seeking orthodontic treatment were females. Nearly 80% of the study sample had narrow palate followed by 15 and 5% of intermediate palate and broad palate, respectively. In regard to arch form, almost 90% of subjects were with tapered maxillary arch form and 10% of them with oval arch form. Males had increased dimensions compared to females, with significant differences, except in palatal depth in the molar area, and palatine height index, in which females showed increased dimensions than males but the differences were statistically non-significant. A strong positive correlation was observed between arch form and canine depth. In regard to arch perimeter, a strong negative correlation was found with molar depth and a medium positive correlation with each of canine depth, palatal width, and palatal length.

Background and Objectives: Orthodontics is a field that has seen significant advancements in recent years, with technology playing a crucial role in improving diagnosis and treatment planning. The study aimed to implement artificial intelligence to predict the arch width as a preventive measure to avoid future crowding in growing patients or even in adult patients seeking orthodontic treatment as a tool for orthodontic diagnosis. Materials and Methods: Four hundred and fifty intraoral scan (IOS) images were selected from orthodontic patients seeking treatment in private orthodontic centers. Real inter-canine, inter-premolar, and inter-molar widths were measured digitally. Two of the main machine learning models were used: the Python programming language and machine learning algorithms, implementing the data on k-nearest neighbor and linear regression. Results: After the dataset had been implemented on the two ML algorithms, linear regression and k-nearest neighbor, the evaluation metric shows that KNN gives better prediction accuracy than LR does. The resulting accuracy was around 99%. Conclusions: it is possible to leverage machine learning to enhance orthodontic diagnosis and treatment planning by predicting linear dental arch measurements and preventing anterior segment malocclusion.

It is impossible to remove tooth-colored restorations by mechanical means without unnecessary damage to the adjacent sound tooth structure. This study is aimed at investigating erbium-doped yttrium aluminum garnet (Er: YAG) laser (Hoya ConBio, VersaWave, CA, USA) in removing composite resin restorations and assessing the change in morphology of bonding surfaces using a scanning electron microscope (EDX, CAMSCANNER, 3200LV, UK). The investigators collected thirty extracted sound human premolar teeth for this investigation, and the conventional design class V cavity was prepared on the buccal surface of each specimen. The specimens were allocated randomly into three groups, according to the procedure used for the ablation of the composite restoration: group A (high-speed diamond fissure bur), group B, and group C (Er: YAG laser) using a different pulse repetition rate of 20 Hz (group B) and 25 Hz (group C). The AutoCAD software program (Autodesk, Inc., 2016) was used to calculate the surface area and the resulting dimensional change of the cavities after restoration removal. The cavities were filled with composite resin and randomly assigned into two groups conforming to the methods applied to eliminate the restoration; diamond turbine fissure bur and laser. In each group, two specimens were selected randomly for scanning electron microscope analysis of bonding surfaces. The least meantime for the composite resin removal was observed in the high-speed diamond bur, significantly less than both Er-YAG laser groups ( $p<0.001$ ). However, at a higher pulse repetition rate, time-consuming decreased. The results showed that laser is more conservative in removing composite resin restoration as the change was most remarkable in group A (0.800 mm), then group C (0.466 mm), and the slightest change is in group B (0.372 mm) ( $p=0.014$ ). The dentin surface of group A showed a smooth surface with no opened dentinal tubule and intact smear layer. In groups B and C, dentin surfaces were irregular, scaly, or flaky, and dentinal tubules were opened without a smear layer. Therefore, Er: YAG laser is effective for composite resin removal considering the parameters chosen in this study with fewer changes in cavity surface area and better microretentive features.

(1) Background: Accurate diagnosis and treatment plans in orthodontics were facilitated by novel technologies. The prediction of occlusal problems is of utmost importance for clinicians. This present study aimed to find any possible correlation between unilateral and bilateral palatally impacted maxillary canine, palatal dimensions, and maxillary arch perimeter using digital measurements and determine the factors that could be used as predictors for maxillary palatal canine impaction. (2) Method: A cross-sectional study was conducted on cone-beam computed tomography images of orthodontic patients aged 15 to 25. Palatal dimensions and maxillary arch perimeter were digitally measured using Materialize Interactive Medical Image Control System. (3) Results: A significant difference was found between the case and the study groups regarding palatal depth, length, and arch perimeter, except for the palatal width. A weak correlation was reported in palatal depth and length with canine impaction, whereas the correlation was moderate concerning the arch perimeter. They can be used as predictors for palatal canine impaction. (4) Conclusion: Palatal dimensions and dental arch perimeter affect maxillary palatal canine impaction. Deficient arch perimeter, palatal depth, and length could be helpful in the prediction of maxillary palatal canine impaction.