Correlation of Three Dimensions of Palate with Maxillary Arch Form and Perimeter as Predictive Measures for Orthodontic and Orthognathic Surgery

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

Impact of ethanol-assisted and non ethanol-assisted mixing methods on the mechanical properties of impregnated polymethylmethacrylate with MgO and Ag nanoparticles

This study aims to elucidate the effect of non ethanol-assisted and ethanol-assisted mixing methods and adding MgO-and Ag-nanoparticles (NPs) into PMMA on flexural strength, impact strength, microhardness (HV) and compressive strength. NPs (1%, 3% and 5% concentrations) were mixed with poly (methyl methacrylate) (PMMA) powder by either using ethanol as a solvent (ethanol-assisted) or without ethanol (non ethanol-assisted). A total of 91 specimens were examined. One- and Two-way ANOVA tests were used to find the effect of mixing methods and concentration of NPs on mechanical properties of PMMA. The results showed an increase of flexural strength for all NPs concentrations (except 1% MgO-NPs) and HV (5% both NPs) in ethanol-assisted groups compared to non ethanol-assisted group (p < 0.05). Furthermore, the combined effects of NPs and mixing methods revealed statistically significant increases in flexural strength and HV in ethanol-assisted group (except in 1% and 3% MgO-NPs) compared to the control group. Meanwhile, no statistically significant differences were detected in impact strength and compressive strength between ethanol-assisted and non ethanol-assisted groups (p > 0.05). The combined effects of NPs and mixing methods presented a statistically significant increase only in compressive strength of 5% of both NPs in ethanol-assisted group in comparison to the control group. Ethanol-assisted mixing of MgO-NPs and Ag-NPs with PMMA showed an increase in the mechanical properties of flexural strength, HV and compressive strength compared to non ethanol-assisted, whereas no improvement in the impact strength property of PMMA was detected. Furthermore, synergetic effects of adding NPs and mixing methods were identified

Prediction of the Dental Arch Perimeter in a Kurdish Sample in Sulaimani City Based on Other Linear Dental Arch Measurements as a Malocclusion Preventive Measure

The current study aimed to find a prediction equation to estimate the arch perimeter (AP) depending on various arch dimensions including intercanine width (ICW), intermolar width (IMW), interpremolar width (IPMW), and arch length (AL) in a sample of the Kurdish population in Sulaimani City. The study sample was 100 pairs of preorthodontic dental casts. Calculations of dental arch dimensions and perimeter were performed by a digital vernier. Statistical analysis was performed via using the SPSS version 25 software. The developed prediction equation for the upper arch was Y=+1.3×arch length+1×intermolar width, whereas the equation for the lower arch was Y=+0.9×intermolar width+0.92×intercanine width. Paired t-test revealed no statistical difference between predicted and real arch perimeters. Two separate prediction equations for upper and lower arches were developed based on the arch length (AL) and intermolar width (IMW) for the maxillary arch, intermolar (IMW), and inter canine widths (ICW) for the lower arch. The developed equations could have further beneficial impacts on orthodontic diagnosis and treatment planning

Orthodontic Implementation of Machine Learning Algorithms for Predicting Some Linear Dental Arch Measurements and Preventing Anterior Segment Malocclusion: A Prospective Study

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