3D Dental Models Research Articles

Diagnostic validity of ICDAS clinical criteria on digital 3D models

ObjectiveTo assess the diagnostic validity of ICDAS clinical criteria on 3D dental models from intraoral scanning. MethodsThis is a retrospective analysis on part of the baseline sample collected in a cohort study and included 73 participants (12–19 years) from Denmark and Greece. The assessment was made by visual inspection, and then by visual inspection associated with radiographs. All participants were scanned with TRIOS 4 which uses white light to obtain the 3D models with tooth color, as well as blue light source (415 nm) for fluorescence. The 3D models’ evaluation was conducted using tooth-color texture and subsequently fluorescence. Two scores were obtained for the 3D model examination: i) ICDAS based on tooth-color information; ii) ICDAS based on tooth-color information supplemented with fluorescence. For the analysis, weighted kappa, sensitivity (SE), specificity (SP) and accuracy (ACC) were calculated. ResultsRegarding all lesions the values for SE, SP, and ACC were respectively 0.804, 0.801, and 0.802 for tooth-color, and 0.819, 0.808, and 0.810 for tooth-color supplemented with fluorescence. In terms of accuracy parameters for moderate-extensive lesions, the values for SE, SP, and ACC for tooth color were 0.709, 0.948, and 0.944, while for fluorescence they were 0.815, 0.937, and 0.934. ConclusionCaries assessment with ICDAS criteria on 3D dental models produces reliable scores. Visual caries analysis using 3D models demonstrates commendable diagnostic accuracy and reasonable consistency with traditional methods. The use of intraoral scanners may be beneficial in evaluating occlusal caries. Clinical SignificanceThe importance of this study is to prove the diagnostic accuracy of caries lesions diagnosis made using and intraoral scanner and to offer greater confidence to professionals who use this diagnosis tool in their daily clinical practice. Intraoral scanners demonstrate to be an accurate tool for diagnosing occlusal caries.

A two-dimensional photographic and three-dimensional digital dental model comparative analysis in maxillary anterior teeth

Objective: To assess the accuracy of two-dimensional (2D) photographs in measuring esthetic parameters of the maxillary anterior teeth by comparing them with measurements obtained from three-dimensional (3D) dental models. Methods: A total of one hundred volunteers (49 males, 51 females, aged 18-23 years) were recruited from School and Hospital of Stomatology, Wuhan University from January to February 2024. 3D digital models of their dentitions were obtained using an intraoral scanner, and standardized frontal 2D intraoral photographs were captured with a digital camera. The lengths, widths and width/length ratio of the bilateral incisors, lateral incisors and canines were measured on both the 3D digital models and the 2D intraoral photographs. The width ratios of adjacent maxillary anterior were also calculated on the 2D intraoral photographs and the frontal view of 3D digital models. Results: The widths of lateral incisors [(5.85±0.60) mm] and canines [(4.73±0.71) mm] and the lengths of canines [(8.72±0.96) mm] in the 2D intraoral photographs were significantly lower than those in 3D digital models [(6.65±0.59), (7.76±0.60), (8.90±0.86) mm] (t=-18.24, P<0.001; t=-54.43, P<0.001; t=-4.40, P<0.001), while there were no significant differences in the lengths and widths of the other teeth (P>0.05). The width/length ratios measured from the 2D intraoral photographs for the lateral incisors and canines (0.74±0.08, 0.55±0.08) were significantly lower than those measured in the 3D digital models (0.84±0.09, 0.88±0.09) (t=-19.68, P<0.001; t=-50.21, P<0.001), and the width/length ratio of the central incisors showed no significant difference between the two groups (P>0.05). The width ratios of canines/lateral incisors and lateral incisors/central incisors measured on the 2D intraoral photographs (0.72±0.06, 0.85±0.11) were significantly smaller than those measured in the frontal view of 3D digital models (0.75±0.06, 0.89±0.11) (t=-9.31, P<0.001; t=-6.58, P<0.001). Conclusions: There is a difference between 2D and 3D measurement results of teeth in the esthetic area and the magnitude of the difference varies with their position in the dental arch. When analyzing the measurement of the anterior teeth, it is necessary to choose the appropriate method according to the target tooth position.

Integration of autonomous maximal smile 3D image with digital 3D dental model and investigation of its accuracy.

This study aims to establish an approach to integrate autonomous maximal smile (AMS) 3D facial image with digital 3D dental models to demonstrate the digital orthodontic set-up in the 3D facial context. Using Geomagic Studio software, the AMS 3D facial image and pre-treatment dental model were manually and globally registered. Subsequently, the pre-treatment dental model was substituted with the predicted post-treatment dental model. The intraoral region of the AMS 3D facial image was removed, achieving a conjunctive display of the AMS 3D facial image and the post-treatment dental set-up. The distances between four groups of corresponding landmark pairs on the AMS 3D facial image and the pre-treatment dental set-up were calculated, and the accuracy of the registration operation was evaluated by paired t-test. The novel approach effectively facilitated the integration of AMS 3D facial images with the pre-treatment and predicted post-treatment 3D dental models. The average distances between the pairs of points were (1.19±0.55) mm and (1.55±0.59) mm for the two registrations, respectively. Notably, no statistically significant difference was observed between the two measurements (P>0.05), indicating a high agreement (intraclass correlation coefficient=0.914). This study established an approach to integrate AMS 3D facial images with digital 3D dental models. Through this approach, the digital orthodontic set-up design can be displayed in the context of a 3D facial image, which may help to improve the quality of outcome set-up in digital orthodontics, such as clear aligner therapy.

Chemical treatment in 3D dental model production for clear aligners via additive manufacturing: a comprehensive evaluation

PurposeStereolithography (SLA) additive manufacturing (AM) technique has enabled the production of inconspicuous and aesthetically pleasing orthodontics that are also hygienic. However, the staircase effect poses a challenge to the application of invisible orthodontics in the dental industry. The purpose of this study is to implement chemical postprocessing technique by using isopropyl alcohol as a solvent to overcome this challenge.Design/methodology/approachFifteen experiments were conducted using a D-optimal design to investigate the effect of different concentrations and postprocessing times on the surface roughness, material removal rate (MRR), hardness and cost of SLA dental parts required for creating a clear customized aligner, and a container was constructed for chemical treatment of these parts made from photocurable resin.FindingsThe study revealed that the chemical postprocessing technique can significantly improve the surface roughness of dental SLA parts, but improper selection of concentration and time can lead to poor surface roughness. The optimal surface roughness was achieved with a concentration of 90 and a time of 37.5. Moreover, the dental part with the lowest concentration and time (60% and 15 min, respectively) had the lowest MRR and the highest hardness. The part with the highest concentration and time required the greatest budget allocation. Finally, the results of the multiobjective optimization analysis aligned with the experimental data.Originality/valueThis paper sheds light on a previously underestimated aspect, which is the pivotal role of chemical postprocessing in mitigating the adverse impact of stair case effect. This nuanced perspective contributes to the broader discourse on AM methodologies, establishing a novel pathway for advancing the capabilities of SLA in dental application.

Establishment of a 3D esthetic analysis workflow on 3D virtual patient and preliminary evaluation

BackgroundIn esthetic dentistry, a thorough esthetic analysis holds significant role in both diagnosing diseases and designing treatment plans. This study established a 3D esthetic analysis workflow based on 3D facial and dental models, and aimed to provide an imperative foundation for the artificial intelligent 3D analysis in future esthetic dentistry.MethodsThe established 3D esthetic analysis workflow includes the following steps: 1) key point detection, 2) coordinate system redetermination and 3) esthetic parameter calculation. The accuracy and reproducibility of this established workflow were evaluated by a self-controlled experiment (n = 15) in which 2D esthetic analysis and direct measurement were taken as control. Measurement differences between 3D and 2D analysis were evaluated with paired t-tests.Results3D esthetic analysis demonstrated high consistency and reliability (0.973 < ICC < 1.000). Compared with 2D measurements, the results from 3D esthetic measurements were closer to direct measurements regarding tooth-related esthetic parameters (P<0.05).ConclusionsThe 3D esthetic analysis workflow established for 3D virtual patients demonstrated a high level of consistency and reliability, better than 2D measurements in the precision of tooth-related parameter analysis. These findings indicate a highly promising outlook for achieving an objective, precise, and efficient esthetic analysis in the future, which is expected to result in a more streamlined and user-friendly digital design process.This study was registered with the Ethics Committee of Peking University School of Stomatology in September 2021 with the registration number PKUSSIRB-202168136.

DilatedToothSegNet: Tooth Segmentation Network on 3D Dental Meshes Through Increasing Receptive Vision

The utilization of advanced intraoral scanners to acquire 3D dental models has gained significant popularity in the fields of dentistry and orthodontics. Accurate segmentation and labeling of teeth on digitized 3D dental surface models are crucial for computer-aided treatment planning. At the same time, manual labeling of these models is a time-consuming task. Recent advances in geometric deep learning have demonstrated remarkable efficiency in surface segmentation when applied to raw 3D models. However, segmentation of the dental surface remains challenging due to the atypical and diverse appearance of the patients’ teeth. Numerous deep learning methods have been proposed to automate dental surface segmentation. Nevertheless, they still show limitations, particularly in cases where teeth are missing or severely misaligned. To overcome these challenges, we introduce a network operator called dilated edge convolution, which enhances the network’s ability to learn additional, more distant features by expanding its receptive field. This leads to improved segmentation results, particularly in complex and challenging cases. To validate the effectiveness of our proposed method, we performed extensive evaluations on the recently published benchmark data set for dental model segmentation Teeth3DS. We compared our approach with several other state-of-the-art methods using a quantitative and qualitative analysis. Through these evaluations, we demonstrate the superiority of our proposed method, showcasing its ability to outperform existing approaches in dental surface segmentation.

Cost-Effective Full-Color 3D Dental Imaging Based on Close-Range Photogrammetry.

Dental imaging plays a crucial role in clinical dental practice. Conventional 2D dental imaging serves general-purpose tasks, such as patient documentation, while high-precision 3D dental scanning is tailored for specialized procedures, such as orthodontics and implant surgeries. In this study, we aimed to develop a cost-effective 3D imaging technique that could bridge the gap between conventional dental photography and high-precision 3D dental scanning, with the goal of improving patient dental care. We developed a 3D imaging technique based on close-range photogrammetry and termed it close-range photogrammetry-based dental imaging (CPDI). We evaluated this technique on both in vitro dental models and in vivo teeth. For dental models, we conducted a parametric study to examine the effects of the depth of field and specular reflection on reconstruction quality. We showed that the optimal results were achieved with an f/5.6 lens and without a circular polarizer for reflection suppression. This configuration generated 3D scans with 57.7 ± 3.2% and 82.4 ± 2.7% of reconstructed points falling within ±0.1 mm and ±0.2 mm error margins, respectively. With such accuracy, these 3D dental models can faithfully represent dental morphology and features. During in vivo imaging, we were able to reconstruct high-quality 3D models of the anterior arch, further demonstrating its clinical relevance. The reconstructed models carry both 3D shapes and detail full-color surface textures, which positions CPDI as a versatile imaging tool in different areas of clinical dental care.

TUCNet: A channel and spatial attention-based graph convolutional network for teeth upsampling and completion

With the increasing popularity of the use of 3D scanning equipment in capturing oral cavity in dental health applications, the quality of 3D dental models has become vital in oral prosthodontics and orthodontics. However, the point cloud data obtained can often be sparse and thus missing information. To address this issue, we construct a high-resolution teeth point cloud completion method named TUCNet to fill up the sparse and incomplete oral point cloud collected and output a dense and complete teeth point cloud. First, we propose a Channel and Spatial Attentive EdgeConv (CSAE) module to fuse local and global contexts in the point feature extraction. Second, we propose a CSAE-based point cloud upsample (CPCU) module to gradually increase the number of points in the point clouds. TUCNet employs a tree-based approach to generate complete point clouds, where child points are derived through a splitting process from parent points following each CPCU. The CPCU learns the up-sampling pattern of each parent point by combining the attention mechanism and the point deconvolution operation. Skip connections are introduced between CPCUs to summarize the split mode of the previous layer of CPCUs, which is used to generate the split mode of the current CPCUs. We conduct numerous experiments on the teeth point cloud completion dataset and the PCN dataset. The experimental results show that our TUCNet not only achieves the state-of-the-art performance on the teeth dataset, but also achieves excellent performance on the PCN dataset.

A systematic review of methodology for the creation of virtual 3D models for use in dental education and a narrative review.

Virtual Reality (VR) technologies can be used as a content-delivery system for the purposes of both entertainment and education. Remote and digital education has become ever so important in a world where global disruptive events such as pandemics and natural disasters can define access to a face-to-face learning environment. An important aspect of VR technologies for dentistry is the creation of digital 3D models. The primary of this review was to answer the focused research question, "What software techniques are used in the creation of digital 3D models for use in dental education." This study systematically evaluates current software and techniques used for creating digital 3D models in dental education using the Preferred Reporting Items for Systematic Reviews (PRIMSA). The search strategies did not find any studies specific to the creation of dental-related 3D models. Therefore, this study for the first time provided an overview of common techniques of 3D model fabrication is discussed. Further some examples of methods of creating 3D models relevant to dentistry such armamentarium and anatomical oral structures have been discussed in considerable detail. The creation of 3D modelling is a rapidly evolving field with software updates and new programs being continually released. This work highlights fundamental lack of published work in the creation of 3D dental models for educational applications.

Diagnostic Applications of Intraoral Scanners: A Systematic Review.

In addition to their recognized value for obtaining 3D digital dental models, intraoral scanners (IOSs) have recently been proven to be promising tools for oral health diagnostics. In this work, the most recent literature on IOSs was reviewed with a focus on their applications as detection systems of oral cavity pathologies. Those applications of IOSs falling in the general area of detection systems for oral health diagnostics (e.g., caries, dental wear, periodontal diseases, oral cancer) were included, while excluding those works mainly focused on 3D dental model reconstruction for implantology, orthodontics, or prosthodontics. Three major scientific databases, namely Scopus, PubMed, and Web of Science, were searched and explored by three independent reviewers. The synthesis and analysis of the studies was carried out by considering the type and technical features of the IOS, the study objectives, and the specific diagnostic applications. From the synthesis of the twenty-five included studies, the main diagnostic fields where IOS technology applies were highlighted, ranging from the detection of tooth wear and caries to the diagnosis of plaques, periodontal defects, and other complications. This shows how additional diagnostic information can be obtained by combining the IOS technology with other radiographic techniques. Despite some promising results, the clinical evidence regarding the use of IOSs as oral health probes is still limited, and further efforts are needed to validate the diagnostic potential of IOSs over conventional tools.

TeethGNN: Semantic 3D Teeth Segmentation with Graph Neural Networks.

In this paper, we present TeethGNN, a novel 3D tooth segmentation method based on graph neural networks (GNNs). Given a mesh-represented 3D dental model in non-Euclidean domain, our method outputs accurate and fine-grained separation of each individual tooth robust to scanning noise, foreign matters (e.g., bubbles, dental accessories, etc.), and even severe malocclusion. Unlike previous CNN-based methods that bypass handling non-Euclidean mesh data by reshaping hand-crafted geometric features into regular grids, we explore the non-uniform and irregular structure of mesh itself in its dual space and exploit graph neural networks for effective geometric feature learning. To address the crowded teeth issues and incomplete segmentation that commonly exist in previous methods, we design a two-branch network, one of which predicts a segmentation label for each facet while the other regresses each facet an offset away from its tooth centroid. Clustering are later conducted on offset-shifted locations, enabling both the separation of adjoining teeth and the adjustment of incompletely segmented teeth. Exploiting GNN for directly processing mesh data frees us from extracting hand-crafted feature, and largely speeds up the inference procedure. Extensive experiments have shown that our method achieves the new state-of-the-art results for teeth segmentation and outperforms previous methods both quantitatively and qualitatively.

Correlation between dental arch form and OSA severity in adult patients: an observational study

BackgroundThe role of interdental widths and palatal morphology on the development of obstructive sleep apnea (OSA) has not been well investigated in adult patients yet. The aim of this paper was to assess the morphology of maxilla and mandibular dental arches on three-dimensional (3D) casts and to correlate these measurements with the severity of OSA.MethodsSixty-four patients (8 women and 56 men, mean age 52.4) with a diagnosis of mild-to-moderate OSA were retrospectively enrolled. On each patient, home sleep apnea test and 3D dental models were collected. Apnea-hypopnea index (AHI) and oxygen desaturation index (ODI) were recorded, as well as the dental measurements including inter-molar distance, anterior and posterior widths of maxillary and mandibular arches, upper and lower arch lengths, palatal height, and palatal surface area. The respiratory and dental variables were then correlated.ResultA statistically inverse correlation was found between ODI and anterior width of lower arch, maxillary arch length, palatal height, and palatal area. AHI showed a significant inverse correlation with anterior width of mandibular arch and maxillary length.ConclusionA significant inverse correlation between maxillary and mandibular morphology and respiratory parameters was shown in the present paper.

Occlusal caries detection on 3D models obtained with an intraoral scanner. A validation study

ObjectivesTo evaluate the diagnostic performance of visual caries assessment on 3D dental models obtained using an intraoral scanner and to compare it with the performance of the clinical visual inspection. MethodsFifty-three permanent posterior teeth scheduled for extraction were randomly selected and included in this study. One to three independent examination sites on the occlusal surface of each tooth were clinically inspected using International Caries Detection and Assessment System (ICDAS) criteria. Afterwards, the examined teeth were scanned intraorally with a 3D intraoral scanner (TRIOS 4, 3Shape TRIOS A/S, Copenhagen, Denmark) using white and blue-violet light (415 nm wavelength) to capture the colour and fluorescence signal from the tissues. Six months after the clinical examination, the same examiner conducted the on-screen assessment of the obtained 3D digital dental models at the selected examination sites using modified ICDAS criteria. Both tooth colour and fluorescence texture with high resolution were assessed. Lastly, an independent examiner conducted the histological examination of all teeth after extraction. Using histology as the reference test, Sensitivity (SE), Specificity (SP), Accuracy (ACC), area under the Receiver Operating Characteristic (ROC) curve, and Spearman's correlation coefficient were calculated for the clinical and on-screen ICDAS assessments. ResultsThe ACC values of the evaluated methods varied between 0.59-0.79 for initial caries lesions and 0.77-0.99 for moderate-extensive caries lesions. Apart from SE values corresponding to caries in the inner half of enamel, no significant difference was observed between clinical visual inspection and on-screen assessment. In addition, no difference was found in the assessment of 3D models with tooth colour alone or supplemented with fluorescence for all the evaluated diagnostic measures. ConclusionsOn-screen visual assessment of 3D digital dental models with tooth colour or fluorescence showed a similar diagnostic performance to the clinical visual inspection when detecting and classifying occlusal caries lesions on permanent teeth. Clinical significance3D intraoral scanning can aid the detection and classification of occlusal caries as part of patient screening and can potentially be used in remote caries assessment for clinical and research purposes.

The associations of dental aesthetics, oral health-related quality of life and satisfaction with aesthetics in an adult population.

The aim of this study was to investigate the gender-specific associations between dental aesthetics, oral health-related quality of life (OHRQoL), and satisfaction with dental aesthetics in an adult population. The study population consisted of 1780 individuals (822 males and 958 females) from the Northern Finland Birth Cohort 1966 (NFBC1966). Dental aesthetics were evaluated from digital 3D dental models using the Aesthetic Component (AC) of the Index of Orthodontic Treatment Need (IOTN). Layperson and orthodontist panels evaluated the dental aesthetics of a smaller sample (n = 100). OHRQoL was measured using the Oral Health Impact Profile (OHIP-14) questionnaire. Satisfaction with dental aesthetics was asked with one separate question. Gender-specific analyses consisted of Mann-Whitney U-tests and Spearman's correlation coefficients. More than half of the population had an aesthetically acceptable occlusion, and most of the individuals were satisfied with the aesthetics. The most severe aesthetic impairments were associated with the psychological dimensions of OHIP-14. There were significant but weak associations of AC and satisfaction with aesthetics, and satisfaction with aesthetics and OHRQoL. Significant gender differences were found, men having higher mean AC scores but women reporting lower OHRQoL. At the population level, most of the individuals were satisfied with their aesthetics, despite different dental aesthetic conditions. The most severe aesthetic impairments were associated with decreased psychological well-being, women reporting more impacts compared to men.

Three‐dimensional dental image segmentation and classification using deep learning with tunicate swarm algorithm

AbstractDentistry frequently makes use of intraoral scanning technologies to digitally acquire the three‐dimensional (3D) geometry of teeth. In recent times, dental clinics over the globe utilize used computer aided diagnosis (CAD) models to make treatment plans, for example, orthodontics. Orthodontic CAD system acts as a vital part of the advanced dentistry field. A 3D dental model, computed by patient impression, as input and aids dentist in the extraction, moving, deletion, and rearranging of teeth to simulate treatment output. Tooth segmentation and labelling is the basic and foremost element of the CAD model which needs to be addressed. Automated segmentation and classification of 3D dental images using advanced machine learning and deep learning (DL) models become essential. This article introduces a new 3D dental image segmentation and classification using DL with tunicate swarm algorithm (3DDISC‐DLTSA) model. The major intention of the 3DDISC‐DLTSA system is to segment the tooth model and identify seven distinct tooth types. To accomplish this, the presented 3DDISC‐DLTSA model performs image pre‐processing in two stages namely image filtering and U‐Net segmentation. In addition, the 3DDISC‐DLTSA model derives DenseNet‐169 model for feature extraction purposes. For the recognition and classification of tooth type, the TSA based hyperparameter tuning process is carried out which helps to accomplish maximum classification performance. A wide range of experimental analyses is performed and the outcomes are inspected under many aspects. On dataset‐1, 3DDISC‐DLTSA model accuracy rose by 96.67%. On dataset‐3, 3DDISC‐DLTSA model accuracy rose by 97.48% and algorithm accuracy by 97.35%. The 3DDISC‐DLTSA model outperformed more modern models, according to the comparative investigation.

Accurate gingival recession quantification using 3D digital dental models

ObjectivesTo develop and validate a method for accurate quantitative assessment of gingival recessions based on superimposition of serial 3D digital models.Materials and methodsGingival recessions of mild (0.5–2 mm) and increased (3–7 mm) severity were simulated on stone casts and surface models were created. The outlines of the gingival margins of the mild (A) and severe recessions (B) were compared to the original gingival margins following 3D best fit superimposition through a gold standard technique (GS), which used intact adjacent structures, and the tested method (CC), which used single tooth crowns at the position of recessions, as superimposition reference. The primary outcome was the distance between the most apical point of each corresponding gingival margin along the respective tooth long axis.ResultsFor mild recessions, the median difference of the test methods (CC_A) from the reference method (GS_A) was 0.008 mm (IQR: 0.093; range: − 0.143, 0.147). For severe recessions, the median difference of the test method (CC_B) from the reference method (GS_B) was 0.009 mm (IQR: 0.091; range: − 0.170, 0.198). The proposed method (CC) showed very high intra- and inter-operator reproducibility (median: 0.025 and 0.033 mm, respectively).ConclusionsThe suggested method offers highly accurate monitoring of gingival margin changes and diagnosis of gingival recessions using 3D digital dental models. The method is applicable irrespective of changes in tooth position or form, allowing for assessments over any time span.Clinical relevanceThe accurate detection and visualization of gingival margin changes in 3D will enhance diagnosis and patient-doctor communication.

3D digital dental models’ accuracy for anthropological study: Comparing close-range photogrammetry to μ-CT scanning

X-rays micro-Computed Tomography (X-μCT) is a non-invasive and non-destructive technique primarily used for the 3D reconstruction of internal/external object structures. Despite the great potential, X-ray exposure for samples of archaeo-anthropological interest can compromise the integrity of the finds, altering the results for future investigations. The aim of this study is to evaluate whether close-range photogrammetry is a viable alternative in terms of non-invasiveness, low cost, efficiency and precision to the 3D external surface models with respect to X-μCT scanning. Photogrammetry was applied to small and highly diagenized archaeological human teeth characterized by irregular and fragmented surfaces. The accuracy of the 3D model and the metric information obtained from photogrammetry was evaluated comparing the micro-measurements obtained by digital caliper and Confocal laser scanning microscope (CLMS). The results obtained show the effectiveness of close-range photogrammetry to create a 3D model with metric information as accurate as those from micro-CT, with the advantage of reproducing textural information.

An Efficient Method of Tooth Segmentation Under Massive Medical Data

To accurately and efficiently complete tooth segmentation from a large amount of oral medical data, the burden of doctors should be reduced. An automatic seed picking method based on 2D projection of the occlusal plane was proposed. First, the authors establish a two-dimensional seed data set for tooth segmentation. Then, this article built a prediction network of teeth seeds based on YOLOv4 to realize the prediction of teeth position as well as the recognition of teeth categories. Finally, according to the statistical optimal seed position, the two-dimensional seeds are calculated and mapped back to the three-dimensional space by the reverse projection transformation method to realize the final picking up of the three-dimensional seeds. Furthermore, combined with the previous work of division line detection, the automatic segmentation of the 3D dental model was realized. The experimental results show that the proposed method has high accuracy and real-time performance, which significantly reduces the burden of human-computer interaction in dental model segmentation.

An Analysis of Maxillary Anterior Teeth Crown Width-Height Ratios: A Photographic, Three-Dimensional, and Standardized Plaster Model's Study.

Objective To analyze the width and height ratios of maxillary anterior teeth at different crown levels through photographs, 3D, and plaster dental model techniques in a subset of the Pakistani population. Material and Methods. This clinical study consisted of 230 participants. The maxillary impression, standardized photographs, and models were constructed for crown width and height analysis. The SPSS version 25 was used for statistical analysis. Descriptive statistics were carried out for mean, standard deviation, and percentage calculation of teeth width and height, gender, and age of participants. Paired t-test analysis was carried out to compare the dependent variables (teeth size, width, and height ratios) with independent variables (techniques applied, side disparity). A p value of ≤ 0.05 was considered statistically significant. Results The mean width and height of maxillary anterior teeth obtained through photographs, 3D, and plater models were statistically different. The 3D dental model analysis showed reliable and accurate results. The mean width and height ratio of teeth were different on both sides of the arch. There was a significant difference (p = 0.001) in crown width-height ratios at different crown levels. Conclusion The width and height ratios in the studied population were different at various crown levels. The dimensions of teeth varied from the incisal to the cervical part of the crown. Hence, rather than relying on a single, fixed ratio of 78% to 80% suggested by researchers for anterior teeth, the clinician should adopt different crown width-height ratios to restore teeth with the optimum esthetic outcome.

Labial Vestibular Flap Closure of the Cleft Palate Is Advantageous for Maxillary Development.

Objective: Using labial vestibular flap was performed to close the primary alveolar and hard palate cleft at the second stage of early 2-stage closure surgery for unilateral cleft lip and palate for minimizing the damage to the maxillary periosteum. We analyzed maxillary development to clarify the influence of cleft palate surgery. Design: Retrospective longitudinal study in 5 years after primary palatal closure. Setting: Institutional study Patients: Study subjects included 214 patients with nonsyndromic complete unilateral cleft lip and palate who were consecutively treated in our clinic. Main Outcome: We used a 3D dental model scanner to assess maxillary development in patients aged 3 months to 5 years after using either the conventional pushback method (PB) (51 cases) or 2-stage closure (Local palatal flap closure: LF [67 cases] and Labial vestibular flap closure: VF [96 cases]). Results: Comparing the measurement results, the major axis of maxilla, width, intercanine distance, and intermolar distance was significantly larger in the LF group compared to the PB group. After the age of 3, the cleft side of VF group had grown significantly to compare with LF group in width. It was also confirmed that the inserted labial mucosal flap itself grew. Enlargement of the labial mucosal flap was observed at all sites except the canine. Conclusion: Good maxillary growth occurred in the following order: VF groups > LF group > PB group. Poor growth was correlated with the extent of periosteal damage during surgery and the degree of postoperative bone surface exposure.