Study on the Association between anterior tooth Colour Preference based on the FAHP and individual factors.

BACKGROUND: The relationship between tooth colour and individual satisfaction in oral aesthetics has long been a topic of interest. In this study, we utilized the fuzzy analytic hierarchy process (FAHP) to investigate the impacts of sex and age on tooth colour preference. The findings of this study should provide a scientific basis for oral aesthetic practice. METHODS: In the current study, a random selection method was employed, and a survey was completed by 120 patients. To obtain tooth colour data, standard tooth colour charts were used. Smile photos were taken as template images using a single-lens reflex camera. The FAHP was utilized to conduct a weight analysis of tooth colour preferences among patients of different sexes and age groups. RESULTS: There were significant differences in tooth colour preference based on sex and age. Men tend to prefer the B1 colour, while women may prioritize the aesthetic effects of other colours. Additionally, as patients age, their preferences for tooth colour become more diverse. These findings offer valuable insights for oral aesthetics practitioners, enabling them to better address the aesthetic needs of patients across different sexes and ages. This knowledge can aid in the development of more personalized treatment plans that align with patients' expectations. CONCLUSION: In this study, we utilized scientific analysis methods to quantify the popularity of different tooth colours among various groups of people. By doing so, we established a scientific foundation for clinical practice. The findings of this study offer valuable insights for oral aesthetic research, enhancing our understanding of tooth colour. Additionally, these findings have practical applications in the field of oral medicine, potentially improving patients' quality of life and overall oral health.

Shape variation in cranium, mandible and teeth in selected mouse strains.

There are different strains of laboratory mouse used in many different fields. These strains differ anatomically. In order to determine these anatomical differences, shape analysis was conducted according to species. CD-1, C57bl/6 and Balb-c strains were preferred to study these differences. Forty-eight adult mouse strains belonging to these strains were utilized. The bones were photographed and geometric morphometry was applied to these photographs. Principal Component Analysis was applied to determine shape variations. In Principal component 1 for cranium, CD-1 and C57bl/6 strain groups showed different shape variations, while Balb-c strain group showed similar shape variations to the other strain groups. Principal Component 1 for the mandible separated the CD-1 and C57bl/6 strain groups in terms of shape variation. Principal Component 2 explained most of the variation between the C57bl/6 and CD-1 lineage groups. In PC1 for molars, the CD-1 group showed a different shape variation from the other groups. Mahalanobis distances and Procrustes distances were measured using Canonical variance analysis to explain the differences between the lineage groups. These measurements were statistically significant. For cranium, in canonical variate 1, CD-1 group of mouse and Balb-c group of mouse were separated from each other. In canonical variate 2, C57bl/6 group of mouse were separated from the other groups. For mandible, Balb-c group of mouse in canonical variate 1 and CD-1 group of mouse in canonical variate 2 were separated from the other groups. For molars, CD-1 group of mouse in canonical variate 1 and Balb-c group of mouse in canonical variate 2 were separated from the other groups. It was thought that these anatomical differences could be caused by genotypic factors as well as dietary differences and many different habits that would affect the way their muscles work.

Bioinformatics for Dentistry: A secondary database for the genetics of tooth development.

Genes strictly regulate the development of teeth and their surrounding oral structures. Alteration of gene regulation leads to tooth disorders and developmental anomalies in tooth, oral, and facial regions. With the advancement of gene sequencing technology, genomic data is rapidly increasing. However, the large sets of genomic and proteomic data related to tooth development and dental disorders are currently dispersed in many primary databases and literature, making it difficult for users to navigate, extract, study, or analyze. We have curated the scattered genetic data on tooth development and created a knowledgebase called 'Bioinformatics for Dentistry' (https://dentalbioinformatics.com/). This database compiles genomic and proteomic data on human tooth development and developmental anomalies and organizes them according to their roles in different stages of tooth development. The database is built by systemically curating relevant data from the National Library of Medicine (NCBI) GenBank, OMIM: Online Mendelian Inheritance in Man, AlphaFold Protein Structure Database, Reactome pathway knowledgebase, Wiki Pathways, and PubMed. The accuracy of the included data was verified from supporting primary literature. Upon data curation and validation, a simple, easy-to-navigate browser interface was created on WordPress version 6.3.2, with PHP version 8.0. The website is hosted in a cloud hosting service to provide fast and reliable data transfer rate. Plugins are used to ensure the browser's compatibility across different devices. Bioinformatics for Dentistry contains four embedded filters for complex and specific searches and free-text search options for quick and simple searching through the datasets. Bioinformatics for Dentistry is made freely available worldwide, with the hope that this knowledgebase will improve our understanding of the complex genetic regulation of tooth development and will open doors to research initiatives and discoveries. This database will be expanded in the future by incorporating resources and built-in sequence analysis tools, and it will be maintained and updated annually.

Fine structural human phantom in dentistry and instance tooth segmentation.

In this study, we present the development of a fine structural human phantom designed specifically for applications in dentistry. This research focused on assessing the viability of applying medical computer vision techniques to the task of segmenting individual teeth within a phantom. Using a virtual cone-beam computed tomography (CBCT) system, we generated over 170,000 training datasets. These datasets were produced by varying the elemental densities and tooth sizes within the human phantom, as well as varying the X-ray spectrum, noise intensity, and projection cutoff intensity in the virtual CBCT system. The deep-learning (DL) based tooth segmentation model was trained using the generated datasets. The results demonstrate an agreement with manual contouring when applied to clinical CBCT data. Specifically, the Dice similarity coefficient exceeded 0.87, indicating the robust performance of the developed segmentation model even when virtual imaging was used. The present results show the practical utility of virtual imaging techniques in dentistry and highlight the potential of medical computer vision for enhancing precision and efficiency in dental imaging processes.

Research Progress on Dental Age Estimation Based on MRI Technology.

Dental age estimation is a crucial aspect and one of the ways to accomplish forensic age estimation, and imaging technology is an important technique for dental age estimation. In recent years, some studies have preliminarily confirmed the feasibility of magnetic resonance imaging (MRI) in evaluating dental development, providing a new perspective and possibility for the evaluation of dental development, suggesting that MRI is expected to be a safer and more accurate tool for dental age estimation. However, further research is essential to verify its accuracy and feasibility. This article reviews the current state, challenges and limitations of MRI in dental development and age estimation, offering reference for the research of dental age assessment based on MRI technology.

Dental Age Estimation in Northern Chinese Han Children and Adolescents Using Demirjian's Method Combined with Machine Learning Algorithms.

OBJECTIVES: To investigate the application value of combining the Demirjian's method with machine learning algorithms for dental age estimation in northern Chinese Han children and adolescents. METHODS: Oral panoramic images of 10 256 Han individuals aged 5 to 24 years in northern China were collected. The development of eight permanent teeth in the left mandibular was classified into different stages using the Demirjian's method. Various machine learning algorithms, including support vector regression (SVR), gradient boosting regression (GBR), linear regression (LR), random forest regression (RFR), and decision tree regression (DTR) were employed. Age estimation models were constructed based on total, female, and male samples respectively using these algorithms. The fitting performance of different machine learning algorithms in these three groups was evaluated. RESULTS: SVR demonstrated superior estimation efficiency among all machine learning models in both total and female samples, while GBR showed the best performance in male samples. The mean absolute error (MAE) of the optimal age estimation model was 1.246 3, 1.281 8 and 1.153 8 years in the total, female and male samples, respectively. The optimal age estimation model exhibited varying levels of accuracy across different age ranges, which provided relatively accurate age estimations in individuals under 18 years old. CONCLUSIONS: The machine learning model developed in this study exhibits good age estimation efficiency in northern Chinese Han children and adolescents. However, its performance is not ideal when applied to adult population. To improve the accuracy in age estimation, the other variables can be considered.

Adolescents and Children Age Estimation Using Machine Learning Based on Pulp and Tooth Volumes on CBCT Images.

OBJECTIVES: To estimate adolescents and children age using stepwise regression and machine learning methods based on the pulp and tooth volumes of the left maxillary central incisor and cuspid on cone beam computed tomography (CBCT) images, and to compare and analyze the estimation results. METHODS: A total of 498 Shanghai Han adolescents and children CBCT images of the oral and maxillofacial regions were collected. The pulp and tooth volumes of the left maxillary central incisor and cuspid were measured and calculated. Three machine learning algorithms (K-nearest neighbor, ridge regression, and decision tree) and stepwise regression were used to establish four age estimation models. The coefficient of determination, mean error, root mean square error, mean square error and mean absolute error were computed and compared. A correlation heatmap was drawn to visualize and the monotonic relationship between parameters was visually analyzed. RESULTS: The K-nearest neighbor model (R2=0.779) and the ridge regression model (R2=0.729) outperformed stepwise regression (R2=0.617), while the decision tree model (R2=0.494) showed poor fitting. The correlation heatmap demonstrated a monotonically negative correlation between age and the parameters including pulp volume, the ratio of pulp volume to hard tissue volume, and the ratio of pulp volume to tooth volume. CONCLUSIONS: Pulp volume and pulp volume proportion are closely related to age. The application of CBCT-based machine learning methods can provide more accurate age estimation results, which lays a foundation for further CBCT-based deep learning dental age estimation research.

Fully automatic AI segmentation of oral surgery-related tissues based on cone beam computed tomography images.

Accurate segmentation of oral surgery-related tissues from cone beam computed tomography (CBCT) images can significantly accelerate treatment planning and improve surgical accuracy. In this paper, we propose a fully automated tissue segmentation system for dental implant surgery. Specifically, we propose an image preprocessing method based on data distribution histograms, which can adaptively process CBCT images with different parameters. Based on this, we use the bone segmentation network to obtain the segmentation results of alveolar bone, teeth, and maxillary sinus. We use the tooth and mandibular regions as the ROI regions of tooth segmentation and mandibular nerve tube segmentation to achieve the corresponding tasks. The tooth segmentation results can obtain the order information of the dentition. The corresponding experimental results show that our method can achieve higher segmentation accuracy and efficiency compared to existing methods. Its average Dice scores on the tooth, alveolar bone, maxillary sinus, and mandibular canal segmentation tasks were 96.5%, 95.4%, 93.6%, and 94.8%, respectively. These results demonstrate that it can accelerate the development of digital dentistry.

Life histories in Fiji as reconstructed from first millennium CE Sigatoka Sand Dune burials using isotopes.

This paper aims to re-examine the dietary practices of individuals buried at Sigatoka Sand Dunes site (Fiji) in Burial Ground 1 excavated by Simon Best in 1987 and 1988 using two approaches and a reassessment of their archaeological, bioarchaeological and chronological frame. First, stable carbon and nitrogen isotope analysis was applied to document dietary changes between childhood and adulthood using an intra-individual approach on paired bone-tooth. Second, the potential adaptation of the individuals to their environment was evaluated through regional and temporal comparisons using inter-individual bone analysis. Ten AMS radiocarbon dates were measured directly on human bone collagen samples, placing the series in a range of approximately 600 years covering the middle of the first millennium CE (1,888 to 1,272 cal BP). δ13C and δ15N ratios were measured on bone and tooth collagen samples from 38 adult individuals. The results show that δ15N values from tooth are higher than those s from bone while bone and tooth δ13C values are similar, except for females. Fifteen individuals were included in an intra-individual analysis based on paired bone and tooth samples, which revealed six dietary patterns distinguished by a differential dietary intake of marine resources and resources at different trophic levels. These highlight sex-specific differences not related to mortuary practices but to daily life activities, supporting the hypothesis of a sexual division of labour. Compared to other Southwest Pacific series, Sigatoka diets show a specific trend towards marine food consumption that supports the hypothesis of a relative food self-sufficiency requiring no interactions with other groups.

Application of deep learning in isolated tooth identification.

BACKGROUND: Teeth identification has a pivotal role in the dental curriculum and provides one of the important foundations of clinical practice. Accurately identifying teeth is a vital aspect of dental education and clinical practice, but can be challenging due to the anatomical similarities between categories. In this study, we aim to explore the possibility of using a deep learning model to classify isolated tooth by a set of photographs. METHODS: A collection of 5,100 photographs from 850 isolated human tooth specimens were assembled to serve as the dataset for this study. Each tooth was carefully labeled during the data collection phase through direct observation. We developed a deep learning model that incorporates the state-of-the-art feature extractor and attention mechanism to classify each tooth based on a set of 6 photographs captured from multiple angles. To increase the validity of model evaluation, a voting-based strategy was applied to refine the test set to generate a more reliable label, and the model was evaluated under different types of classification granularities. RESULTS: This deep learning model achieved top-3 accuracies of over 90% in all classification types, with an average AUC of 0.95. The Cohen's Kappa demonstrated good agreement between model prediction and the test set. CONCLUSIONS: This deep learning model can achieve performance comparable to that of human experts and has the potential to become a valuable tool for dental education and various applications in accurately identifying isolated tooth.

Shed teeth from Portezuelo formation at Sierra del Portezuelo reveal a higher diversity of predator theropods during Turonian-Coniacian times in northern Patagonia.

The study of thirty-two shed crowns from the Portezuelo Formation (middle Turonian-late Coniacian) at the Sierra del Portezuelo locality, reveals six distinct tooth morphotypes identified through cladistic, discriminant, and cluster analyses. Two morphotypes were identified as belonging to Megaraptoridae, three to Abelisauridae, one to Abelisauroidea, and one to Alvarezsauridae. Additionally, two of the morphotypes exhibit a combination of dental features typically found in megaraptorid and abelisauridtheropods. These results suggest a greater diversity of theropods in the original ecosystem than previously thought, including the presence of a second morphotype of megaraptorid and alvarezsaurid previously undocumented in this formation. Furthermore, the existence of Morphotype 6 indicates the potential coexistence of medium-sized abelisauroids alongside larger abelisaurids in the same ecosystem. These findings underscore the importance of future expeditions to the Sierra del Portezuelo locality to further our understanding of these previously unknown theropod species.

From Jane Doe to Sofia: DNA Extraction Protocol from Bones and Teeth without Liquid Nitrogen for Identifying Skeletal Remains.

DNA analysis plays a crucial role in forensic investigations, helping in criminal cases, missing persons inquiries, and archaeological research. This study focuses on the DNA concentration in different skeletal elements to improve human identification efforts. Ten cases of unidentified skeletal remains brought to the Institute of Forensic Medicine in Timisoara, Romania, underwent DNA analysis between 2019 and 2023. The results showed that teeth are the best source for DNA extraction as they contain the highest concentration of genetic material, at 3.68 ng/µL, compared to the petrous temporal bone (0.936 ng/µL) and femur bone (0.633 ng/µL). These findings highlight the significance of teeth in forensic contexts due to their abundant genetic material. Combining anthropological examination with DNA analysis enhances the understanding and precision of identifying human skeletal remains, thus advancing forensic science. Selecting specific skeletal elements, such as the cochlea or teeth, emerges as crucial for reliable genetic analyses, emphasizing the importance of careful consideration in forensic identification procedures. Our study concludes that automated DNA extraction protocols without liquid nitrogen represent a significant advancement in DNA extraction technology, providing a faster, more efficient, and less labor-intensive method for extracting high-quality DNA from damaged bone and tooth samples.

Microhardness Measurements on Tooth and Alveolar Bone in Rodent Oral Disease Models.

The mechanical property, microhardness, is evaluated in dental enamel, dentin, and bone in oral disease models, including dental fluorosis and periodontitis. Micro-CT (µCT) provides 3D imaging information (volume and mineral density) and scanning electron microscopy (SEM) produces microstructure images (enamel prism and bone lacuna-canalicular). Complementarily to structural analysis by µCT and SEM, microhardness is one of the informative parameters to evaluate how structural changes alter mechanical properties. Despite being a useful parameter, studies on microhardness of alveolar bone in oral diseases are limited. To date, divergent microhardness measurement methods have been reported. Since microhardness values vary depending on the sample preparation (polishing and flat surface) and indentation sites, diverse protocols can cause discrepancies among studies. Standardization of the microhardness protocol is essential for consistent and accurate evaluation in oral disease models. In the present study, we demonstrate a standardized protocol for microhardness analysis in tooth and alveolar bone. Specimens used are as follows: for the dental fluorosis model, incisors were collected from mice treated with/without fluoride-containing water for 6 weeks; for ligature-induced periodontal bone resorption (L-PBR) model, alveolar bones with periodontal bone resorption were collected from mice ligated on the maxillary 2nd molar. At 2 weeks after the ligation, the maxilla was collected. Vickers hardness was analyzed in these specimens according to the standardized protocol. The protocol provides detailed materials and methods for resin embedding, serial polishing, and indentation sites for incisors and alveolar. To the best of our knowledge, this is the first standardized microhardness protocol to evaluate the mechanical properties of tooth and alveolar bone in rodent oral disease models.

Automatic removal of soft tissue from 3D dental photo scans; an important step in automating future forensic odontology identification.

The potential of intraoral 3D photo scans in forensic odontology identification remains largely unexplored, even though the high degree of detail could allow automated comparison of ante mortem and post mortem dentitions. Differences in soft tissue conditions between ante- and post mortem intraoral 3D photo scans may cause ambiguous variation, burdening the potential automation of the matching process and underlining the need for limiting inclusion of soft tissue in dental comparison. The soft tissue removal must be able to handle dental arches with missing teeth, and intraoral 3D photo scans not originating from plaster models. To address these challenges, we have developed the grid-cutting method. The method is customisable, allowing fine-grained analysis using a small grid size and adaptation of how much of the soft tissues are excluded from the cropped dental scan. When tested on 66 dental scans, the grid-cutting method was able to limit the amount of soft tissue without removing any teeth in 63/66 dental scans. The remaining 3 dental scans had partly erupted third molars (wisdom teeth) which were removed by the grid-cutting method. Overall, the grid-cutting method represents an important step towards automating the matching process in forensic odontology identification using intraoral 3D photo scans.

Three-axis load analysis of high-speed handpiece on dental training teeth and computer-aided design/computer-aided manufacturing blocks.

This study aimed to evaluate and compare the mechanical properties of dental training teeth with subtractive and additive computer-aided design/computer-aided manufacturing (CAD/CAM) materials used to fabricate dental simulation models. Therefore, the three-axis load generated during cutting movements, including drilling and milling performed using a dental handpiece, was measured and compared. The samples were cut vertically downward by 1.5 mm, horizontally by 6 mm, and vertically upward at a constant speed (1 mm/s), while the rotational speed of the bur was maintained at 200,000 rotations per minute. A three-axis load cell was used to measure the X-, Y-, and Z-axis loads on the specimen. The median value of the X-, Y-, and Z-axis measurements and the resultant load during the vertical-downward, horizontal, and vertical-upward movements were compared using a one-way analysis of variance and Tukey's post hoc test. For vertical downward movement, the drilling force of the dental training teeth was lower than that of Vita Enamic® and similar to that of Lava™ Ultimate. In contrast to subtractive CAD/CAM blocks, the drilling force of the dental training teeth was higher than that of 3D-printed resin blocks. Regarding horizontal movement, the milling force of dental training teeth was lower than that of Vita Enamic®. In contrast, the milling force of Nissin was similar to that of Lava™ Ultimate, while that of Frasaco was lower. Furthermore, compared to additive CAD/CAM blocks, the milling force of the dental training teeth was higher than that of 3D-printed resin blocks. Regarding vertical upward movement, the resultant loads of dental training teeth was lower than that of Vita Enamic®. Similarly, the resultant load of Nissin was similar to that of Lava™ Ultimate, while that of Frasaco was lower. Additionally, compared to additive CAD/CAM blocks, the resultant loads of the dental training teeth were similar to those of the 3D-printed resin blocks.

Nonclassical crystallization of goethite nanorods in limpet teeth by self-assembly of silica-rich nanoparticles reveals structure-mechanical property relations.

The intricate organization of goethite nanorods within a silica-rich matrix makes limpet teeth the strongest known natural material. However, the mineralization pathway of goethite in organisms under ambient conditions remains elusive. Here, by investigating the multi-level structure of limpet teeth at different growth stages, it is revealed that the growth of goethite crystals proceeds by the attachment of amorphous nanoparticles, a nonclassical crystallization pathway widely observed during the formation of calcium-based biominerals. Importantly, these nanoparticles contain a high amount of silica, which is gradually expelled during the growth of goethite. Moreover, in mature teeth of limpet, the content of silica correlates with the size of goethite crystals, where smaller goethite crystals are densely packed in the leading part with higher content of silica. Correspondingly, the leading part exhibits higher hardness and elastic modulus. Thus, this study not only reveals the nonclassical crystallization pathway of goethite nanorods in limpet teeth, but also highlights the critical roles of silica in controlling the hierarchical structure and the mechanical properties of limpet teeth, thus providing inspirations for fabricating biomimetic materials with excellent properties.

Determination of Mouth Width for Facial Reconstruction Based on Statistical Regression Model.

INTRODUCTION: It is important to generate predictable statistical models by increasing the number of variables on the human skeletal and soft tissue structures on the face to increase the accuracy of human facial reconstructions. The purpose of this study was to determine mouth width 3-dimensionally based on statistical regression model. MATERIAL AND METHODS: Cone-beam computed tomography scan data from 130 individuals were used to measure the horizontal and vertical dimensions of orbital and nasal structures and intercanine width. The correlation between these hard tissue variables and the mouth width was evaluated using the statistical regression model. RESULTS: Orbital width, nasal width, and intercanine width were found to be strong predictors of the mouth width determination and were used to generate the regression formulae to find the most approximate position of the mouth. CONCLUSION: These specific variables may contribute to improving the accuracy of mouth width determination for oral and maxillofacial reconstructions.