Tooth Segmentation Research Articles

Automated Tooth Segmentation in Magnetic Resonance Scans Using Deep Learning.

The main objective was to develop and evaluate an artificial intelligence (AI) model for tooth segmentation in magnetic resonance (MR) scans. MR scans of 20 patients performed with a commercial 64-channel head coil with a T1-weighted 3D-SPACE (Sampling Perfection with Application Optimized Contrasts using different flip angle Evolution) sequence were included. 16 datasets were used for model training and 4 for accuracy evaluation. Two clinicians segmented and annotated the teeth in each dataset. A segmentation model was trained using the nnU-Net framework.The manual reference tooth segmentation and the inferred tooth segmentation were superimposed and compared by computing precision, sensitivity, and Dice-Sørensen coefficient. Surface meshes were extracted from the segmentations, and the distances between points on each mesh and their closest counterparts on the other mesh were computed, of which the mean (average symmetric surface distance, ASSD) and 95th percentile (Hausdorff distance 95%, HD95) were reported. The model achieved an overall precision of 0.867, a sensitivity of 0.926, a Dice-Sørensen coefficient of 0.895, and a 95% Hausdorff distance of 0.91 mm. The model predictions were less accurate for datasets containing dental restorations due to image artefacts. The current study developed an automated method for tooth segmentation in MR scans with moderate to high effectiveness for scans with respectively without artefacts.

Transformer based 3D tooth segmentation via point cloud region partition

Automatic and accurate tooth segmentation on 3D dental point clouds plays a pivotal role in computer-aided dentistry. Existing Transformer-based methods focus on aggregating local features, but fail to directly model global contexts due to memory limitations and high computational cost. In this paper, we propose a novel Transformer-based 3D tooth segmentation network, called PointRegion, which can process the entire point cloud at a low cost. Following a novel modeling of semantic segmentation that interprets the point cloud as a tessellation of learnable regions, we first design a RegionPartition module to partition the 3D point cloud into a certain number of regions and project these regions as embeddings in an effective way. Then, an offset-attention based RegionEncoder module is applied on all region embeddings to model global context among regions and predict the class logits for each region. Considering the irregularity and disorder of 3D point cloud data, a novel mechanism is proposed to build the point-to-region association to replace traditional convolutional operations. The mechanism, as a medium between points and regions, automatically learns the probabilities that each point belongs to its neighboring regions from the similarity between point and region features, achieving the goal of point-level segmentation. Since the number of regions is far less than the number of points, our proposed PointRegion model can leverage the capability of the global-based Transformer on large-scale point clouds with low computational cost and memory consumption. Finally, extensive experiments demonstrate the effectiveness and superiority of our method on our dental dataset.

Spatial Prior-Guided Bi-Directional Cross-Attention Transformers for Tooth Instance Segmentation.

Tooth instance segmentation of dental panoramic X-ray images is of significant clinical importance. Teeth exhibit symmetry within the upper and lower jawbones and are arranged in a specific order. However, previous studies frequently overlook this crucial spatial prior information, resulting in the misidentifications of tooth categories, especially for adjacent or similarly shaped teeth. In this paper, we propose SPGTNet, a spatial prior-guided transformer method, designed to both the extracted tooth positional features from CNNs and the long-range contextual information from vision transformers, specifically for dental panoramic X-ray image segmentation. Initially, a center-based spatial prior perception module is employed to identify the centroid of each tooth, thereby enhancing the spatial prior information for the CNN sequence features. Subsequently, a bi-directional cross-attention module is designed to facilitate the interaction between the spatial prior information of the CNN sequence features and the long-range contextual features of the vision transformer sequence features. Finally, an instance identification head is employed to generate the tooth segmentation results. Extensive experiments on three public benchmark datasets demonstrate the effectiveness and superiority of our proposed method compared to other state-of-the-art approaches. The proposed method accurately identifies and analyzes tooth structures, thereby providing crucial information for dental diagnosis, treatment planning, and research.

Automatic 3-dimensional quantification of orthodontically induced root resorption in cone-beam computed tomography images based on deep learning

Orthodontically induced root resorption (OIRR) is a common and undesirable consequence of orthodontic treatment. Traditionally, studies employ manual methods to conduct 3-dimensional quantitative analysis of OIRR via cone-beam computed tomography (CBCT), which is often subjective and time-consuming. With advancements in computer technology, deep learning-based approaches have gained traction in medical image processing. This study presents a deep learning-based model for the fully automatic extraction of root volume information and the localization of root resorption from CBCT images. In this cross-sectional, retrospective study, 4534 teeth from 105 patients were used to train and validate an automatic model for OIRR quantification. The protocol encompassed several steps: preprocessing of CBCT images involving automatic tooth segmentation and conversion into point clouds, followed by segmentation of tooth crowns and roots via the Dynamic Graph Convolutional Neural Network. The root volume was subsequently calculated, and OIRR localization was performed. The intraclass correlation coefficient was employed to validate the consistency between the automatic model and manual measurements. The proposed method strongly correlated with manual measurements in terms of root volume and OIRR severity assessment. The intraclass correlation coefficient values for average volume measurements at each tooth position exceeded 0.95 (P<0.001), with the accuracy of different OIRR severity classifications surpassing 0.8. The proposed methodology provides automatic and reliable tools for OIRR assessment, offering potential improvements in orthodontic treatment planning and monitoring.

ADGRU: Adaptive DenseNet with gated recurrent unit for automatic diagnosis of periodontal bone loss and stage periodontitis with tooth segmentation mechanism.

Periodontics and gingivitis are two of the most widely prevalent illnesses that affect people nowadays. The sixth most common disease in the world is periodontitis, and detecting periodontal bone loss is essential in the earlier condition and is crucial for the development of the proper diagnosis. Early bone loss detection can be assisted by using computer-assisted radiography examination. Understanding disease progression helps to select the most effective treatment action. An effective deep model is suggested to detect periodontal bone loss at an earlier stage for preventing the progression of Periodontics bone loss. This work is intimated by collecting images from online resources. Further, the images gathered from the dataset are preceded by the tooth segmentation which is done using DenseUNet + + . Further, the segmented images are given to the Adaptive DenseNet with Gated Recurrent Unit (AD-GRU) for detecting periodontal bone loss and this diagnosis is used for the periodontitis stage, where the ADGRU performance is augmented by optimizing the attributes using the Refined Red Kite Optimization Algorithm (RRKOA). The offered approach attained an accuracy of 94.45% which is higher than the88.63%, 90.58%, 89.54%, and 92.96% attained by the LSTM, DenseNet, GRU, DenseNet-GRU. The findings of the simulation proved the designed framework outperformed the traditional model with high accuracy. The developed effectual deep model-based periodontal bone loss and stage periodontitis diagnosis structure is used in healthcare applications.

Periapical lesion detection in periapical radiographs using the latest convolutional neural network ConvNeXt and its integrated models

To overcome the limitation of a single classification model’s inability to simultaneously identify multiple lesion targets within periapical radiographs, This study proposes YoCNET (Yolov5 + ConvNeXt), a novel deep learning integrated model. YoCNET leverages the target detection capability of Yolov5 and the image classification capability of ConvNeXt to achieve automatic segmentation of individual teeth and concurrent detection of periapical lesions across multiple teeth. A dataset of 1,305 periapical radiographs was used to train and validate the ConvNeXt and ResNet34 models, with an 8:2 split for training and validation. Deciduous teeth were excluded from the dataset. Furthermore, 717 individual teeth images were extracted from 200 previously unused periapical radiographs for integrated model validation. Evaluation metrics included accuracy, precision, sensitivity, F1 score, AUC (Area Under Curve), and a confusion matrix.The YoCNET integrated model demonstrated values of 90.93%, 98.88%, 85.30%, 0.9159, and 0.9757 for accuracy, precision, sensitivity, F1 score, and AUC, respectively. These metrics were superior to those achieved by the YoRNET (Yolov5 + ResNet34) integrated model, which recorded 80.47%, 83.78%, 82.16%, 0.8296, and 0.8822. The integrated model achieved high accuracy and efficiency in automatic teeh segmentation by Yolov5 and in automatically detecting multiple periapical lesions by ConvNeXt. YoCNET exhibited superior overall data performance, making it a more suitable deep learning integrated model for clinical applications.

Tooth segmentation by low-dose CBCT for orthodontic treatment planning : Explorative ex vivo validation.

Three-dimensional imaging has become an increasingly important component of orthodontics. Associated with this, however, is ahigher radiation exposure for patients. New cone-beam computed tomography (CBCT) devices have been developed that can provide low-dose CBCT(LD-CBCT). We hypothesized that LD-CBCT is as precise and reproducible as standard high-dose CBCT (HD-CBCT) in segmenting roots and crowns as well as measuring tooth length. HD-CBCT and LD-CBCT scans were taken of four human cadaveric heads. Thirty single-rooted teeth were segmented twice by one investigator. The length of each tooth was also measured. Lin's concordance correlation coefficient (CCC) was calculated to assess the agreement of HD-CBCT and LD-CBCT measurements and the intraclass correlation coefficient (ICC) was calculated to assess intrarater reliability. Analyses were supported by Bland-Altman plots. Volume measurements obtained using HD-CBCT were significantly higher than those obtained using LD-CBCT (p < 0.001). CCC was 0.975 (95% confidence interval [CI] = 0.956-0.986) indicating excellent agreement between the two modalities. Intrarater reliability between the two sets of LD-CBCT and HD-CBCT volume measurements was excellent (ICC = 0.998, 95%CI = 0.995-0.999 [HD-CBCT], ICC = 0.997, 95%CI = 0.992-0.998 [LD-CBCT]). CCC for tooth length measurements was 0.991 (95% CI = 0.983-0.995), indicating excellent agreement between HD-CBCT and LD-CBCT. Intrarater reliabilities between the two sets of tooth length measurements were also excellent for both methods (ICC = 0.998, 95%CI = 0.995-0.999 [HD-CBCT], ICC = 0.997, 95%CI = 0.992-0.998 [LD-CBCT]). Within the limitations of this experimental setting, LD-CBCT is as valid as HD-CBCT for measuring tooth length. Regarding the volume differences, in vivo studies are required to determine their clinical relevance.

Publicly Available Dental Image Datasets for Artificial Intelligence.

The development of artificial intelligence (AI) in dentistry requires large and well-annotated datasets. However, the availability of public dental imaging datasets remains unclear. This study aimed to provide a comprehensive overview of all publicly available dental imaging datasets to address this gap and support AI development. This observational study searched all publicly available dataset resources (academic databases, preprints, and AI challenges), focusing on datasets/articles from 2020 to 2023, with PubMed searches extending back to 2011. We comprehensively searched for dental AI datasets containing images (intraoral photos, scans, radiographs, etc.) using relevant keywords. We included datasets of >50 images obtained from publicly available sources. We extracted dataset characteristics, patient demographics, country of origin, dataset size, ethical clearance, image details, FAIRness metrics, and metadata completeness. We screened 131,028 records and extracted 16 unique dental imaging datasets. The datasets were obtained from Kaggle (18.8%), GitHub, Google, Mendeley, PubMed, Zenodo (each 12.5%), Grand-Challenge, OSF, and arXiv (each 6.25%). The primary focus was tooth segmentation (62.5%) and labeling (56.2%). Panoramic radiography was the most common imaging modality (58.8%). Of the 13 countries, China contributed the most images (2,413). Of the datasets, 75% contained annotations, whereas the methods used to establish labels were often unclear and inconsistent. Only 31.2% of the datasets reported ethical approval, and 56.25% did not specify a license. Most data were obtained from dental clinics (50%). Intraoral radiographs had the highest findability score in the FAIR assessment, whereas cone-beam computed tomography datasets scored the lowest in all categories. These findings revealed a scarcity of publicly available imaging dental data and inconsistent metadata reporting. To promote the development of robust, equitable, and generalizable AI tools for dental diagnostics, treatment, and research, efforts are needed to address data scarcity, increase diversity, mandate metadata completeness, and ensure FAIRness in AI dental imaging research.

A prospective randomized study on the efficacy of real-time dynamic navigation in deep horizontal mandibular third molar extractions

PurposeThis study aimed to evaluate the clinical efficacy of applying real-time dynamic navigation (RDN) in the extraction of deep horizontal mandibular impacted third molars, hypothesizing that RDN reduces surgical time and minimizes the risk of injury to adjacent anatomical structures.MethodsA prospective study was conducted on 160 patients aged between 18 and 37 years with deep horizontal impaction of the mandibular third molar. The participants were randomly assigned to either the experimental group (receiving RDN-assisted extractions) or the control group (undergoing traditional extraction methods). Preoperative planning utilized cone beam computed tomography (CBCT) and Mimics software for the accurate localization and segmentation of impacted teeth. Parametric data were analysed via an independent t test for intergroup comparisons, and significance was set to p < 0.05.ResultsIn the experimental group, an average of 11 ± 1 min was required for preoperative planning via RDN, which was not required in the control group. The setup of the navigation system took an average of 4 ± 1 min in the experimental group and 0 min in the control group. The experimental group demonstrated a significantly shorter average surgical time (22 ± 3 min) than did the control group (36 ± 3 min). The differences in the preoperative design time, surgical time, and complication rates between the two groups were statistically significant (p = 0.005). Additionally, the RDN group reported no complications related to adjacent tooth damage or nerve injury.ConclusionThe precision, safety, real-time guidance of RDN supports its use in complicated dental extractions, which would introduce a new era of oral and maxillofacial surgery.Graphical

Accuracy of imaging software usable in clinical settings for 3D rendering of tooth structures.

The aim of the present study was to evaluate the segmentation accuracy of the dentition by testing four open-source semi-automatic software programs. Twenty CBCT scans were selected to perform semi-automatic segmentation of the maxillary and mandibular dentition. The software programs tested were InVesalius, ITK-SNAP, 3D Slicer, and Seg3D. In addition, each tooth model was manually segmented using Mimics software; this was set as the gold standard (GS) reference of the investigation. A specific 3D imaging technology was used to perform the superimposition between the tooth models obtained with the semi-automatic software and the GS model as well as to perform the surface-to-surface matching analysis. The accuracy of semi-automatic segmentation was evaluated, calculating the volumetric mean differences (mean bias and limits of agreement) and the percentage of matching of the tooth models compared with the manual segmentation (GS). Qualitative assessments were performed using color-coded maps. All data were statistically analyzed to perform comparisons between the investigated software programs. Statistically significant differences were found in the volumetric and matching percentage data (P 0.05). InVesalius was the most accurate software program for 3D rendering of the dentition, with a volumetric bias (Mimics software) ranging from 4.59 to 85.79 mm3, while ITK-SNAP showed the highest volumetric bias, ranging from 30.22 to 319.83 mm3. The mismatched area was mainly located at the radicular tooth region. The volumetric data showed excellent inter-software reliability, with coefficient values ranging from 0.951 to 0.997. Different semi-automatic software algorithms could generate different patterns of inaccuracy error in the segmentation of teeth.

LMVSegRNN and Poseidon3D: Addressing Challenging Teeth Segmentation Cases in 3D Dental Surface Orthodontic Scans.

The segmentation of teeth in 3D dental scans is difficult due to variations in teeth shapes, misalignments, occlusions, or the present dental appliances. Existing methods consistently adhere to geometric representations, omitting the perceptual aspects of the inputs. In addition, current works often lack evaluation on anatomically complex cases due to the unavailability of such datasets. We present a projection-based approach towards accurate teeth segmentation that operates in a detect-and-segment manner locally on each tooth in a multi-view fashion. Information is spatially correlated via recurrent units. We show that a projection-based framework can precisely segment teeth in cases with anatomical anomalies with negligible information loss. It outperforms point-based, edge-based, and Graph Cut-based geometric approaches, achieving an average weighted IoU score of 0.97122±0.038 and a Hausdorff distance at 95 percentile of 0.49012±0.571 mm. We also release Poseidon's Teeth 3D (Poseidon3D), a novel dataset of real orthodontic cases with various dental anomalies like teeth crowding and missing teeth.

A dual-labeled dataset and fusion model for automatic teeth segmentation, numbering, and state assessment on panoramic radiographs

BackgroundRecently, deep learning has been increasingly applied in the field of dentistry. The aim of this study is to develop a model for the automatic segmentation, numbering, and state assessment of teeth on panoramic radiographs.MethodsWe created a dual-labeled dataset on panoramic radiographs for training, incorporating both numbering and state labels. We then developed a fusion model that combines a YOLOv9-e instance segmentation model with an EfficientNetv2-l classification model. The instance segmentation model is used for tooth segmentation and numbering, whereas the classification model is used for state evaluation. The final prediction results integrate tooth position, numbering, and state information. The model’s output includes result visualization and automatic report generation.ResultsPrecision, Recall, mAP50 (mean Average Precision), and mAP50-95 for the tooth instance segmentation task are 0.989, 0.955, 0.975, and 0.840, respectively. Precision, Recall, Specificity, and F1 Score for the tooth classification task are 0.943, 0.933, 0.985, and 0.936, respectively.ConclusionsThis fusion model is the first to integrate automatic dental segmentation, numbering, and state assessment. It provides highly accurate results, including detailed visualizations and automated report generation.

Unleashing the potential of applied UNet architectures and transfer learning in teeth segmentation on panoramic radiographs

Accurate tooth segmentation in panoramic radiographs is a useful tool for dentists to diagnose and treat dental diseases. Segmenting and labeling individual teeth in panoramic radiographs helps dentists monitor the formation of caries, detect bone loss due to periodontal disease, and determine the location and orientation of damaged teeth. It can also aid in both the planning and placement of dental implants, as well as in forensic dentistry for the identification of individuals in criminal cases or human remains. With the advancement of artificial intelligence, many deep learning-based methods are being developed and improved. Although convolutional neural networks have been extensively used in medical image segmentation, the UNet and its advanced architectures stand out for their superior segmentation capacities. This study presents four semantic segmentation UNets (Classic UNet, Attention UNet, UNet3+, and Transformer UNet) for accurate tooth segmentation in panoramic radiographs using the new Tufts Dental dataset. Each model was performed using transfer learning from ImageNet-trained VGG19 and ResNet50 models. The models achieved the best results compared to the other literature models with dice coefficients (DC) and intersection over union (IoU) of 94.64% to 96.98% and 84.27% to 94.19%, respectively. This result suggests that Unet and its variants are more suitable for segmenting panoramic radiographs and could be useful for potential dental clinical applications.

Recent Advances in Intraoral Scanners.

Intraoral scanners (IOSs) have emerged as a cornerstone technology in digital dentistry. This article examines the recent advancements and multifaceted applications of IOSs, highlighting their benefits in patient care and addressing their current limitations. The IOS market has seen a competitive surge. Modern IOSs, featuring continuous image capture and advanced software for seamless image stitching, have made the scanning process more efficient. Patient comfort with IOS procedures is favorable, mitigating the discomfort associated with conventional impression taking. There has been a shift toward open data interfaces, notably enhancing interoperability. However, the integration of IOSs into large dental institutions is slow, facing challenges such as compatibility with existing health record systems and extensive data storage management. IOSs now extend beyond their use in computer-aided design and manufacturing, with software solutions transforming them into platforms for diagnostics, patient communication, and treatment planning. Several IOSs are equipped with tools for caries detection, employing fluorescence technologies or near-infrared imaging to identify carious lesions. IOSs facilitate quantitative monitoring of tooth wear and soft-tissue dimensions. For precise tooth segmentation in intraoral scans, essential for orthodontic applications, developers are leveraging innovative deep neural network-based approaches. The clinical performance of restorations fabricated based on intraoral scans has proven to be comparable to those obtained using conventional impressions, substantiating the reliability of IOSs in restorative dentistry. In oral and maxillofacial surgery, IOSs enhance airway safety during impression taking and aid in treating conditions such as cleft lip and palate, among other congenital craniofacial disorders, across diverse age groups. While IOSs have improved various aspects of dental care, ongoing enhancements in usability, diagnostic accuracy, and image segmentation are crucial to exploit the potential of this technology in optimizing patient care.

A Computer-Aided Model for Dental Image Diagnosis Utilizing Convolutional Neural Networks

A Convolutional Neural Network (CNN) is an artificial neural network that is primarily utilized for the purposes of image recognition and processing, owing to its remarkable ability to recognize patterns within images. CNNs have found widespread application in diverse areas of computer vision, including but not limited to object tracking and recognition, security, and military and biomedical image analysis. CNN in orthodontic medical imaging technologies to reduce orthodontic treatment planning time, including automatic landmark search on cephalometric radiographs, cone beam computed tomography (CBCT) tooth segmentation, and CBCT tooth segmentation. This paper describes the strategy and the architecture of deep convolutional neural networks applied to DICOM datasets to distinguish between X-ray pictures with and without teeth. This work focuses on the application of the CNNs to a DICOM dataset in orthodontics as pre-processing for CNNs using fuzzy C-means clustering to construct a reasonable prediction that results in improved accuracy of this system. The aforementioned proposal has shown encouraging outcomes and visual representations, indicating that utilizing methods based on convolutional neural networks can greatly enhance the computational planning of orthodontic treatments by decreasing the time required for analysis. In many cases, this approach's analysis surpasses the accuracy of a manual orthodontist. This model achieved an accuracy exceeding 98% in applying the CNNs to differentiate between X-ray images with teeth and others with no teeth to focus any further work only on useful images which helps in diagnosis planning.

DeepPlaq: Dental plaque indexing based on deep neural networks.

The selection of treatment for dental plaque is closely related to the condition of the plaque on different teeth. This study validated the ability of CNN models in assessing the dental plaque indices. In 70 (20 male and 50 female) healthy adults (18 to 55 years old), frontal and lateral view intraoral images (210) of plaque disclosing agent stained permanent and deciduous dentitions were obtained. A three-stage method was employed, where the You Look Only Once version 8 (YOLOv8) model was first used to detect the target teeth, followed by the prompt-based Segment Anything Model (SAM) segmentation algorithm to segment teeth. A new single-tooth dataset consisting of 1400 photographs was obtained after applying a two-stage method. Finally, a multi-class classification model DeepPlaq was trained and evaluated on the accuracy of dental plaque indexing based on the Quigley-Hein Index (QHI) scoring system. Classification performance was measured using accuracy, recall, precision, and F1-score. The teeth detector exhibited an accuracy (mean average precision, mAP) of approximately 0.941 ± 0.005 in identifying teeth with plaque disclosing agents. The maximum accuracy attained in the plaque indexing through DeepPlaq was 0.84 (probability that DeepPlaq scored identical to experts), and the smallest average scoring error was less than 0.25 on a 0 to 5 scale for scoring. A three-stage approach demonstrated excellent performance in detecting and segmenting target teeth, and DeepPlaq model also showed strong performance in assessing dental plaque indices. Application of artificial intelligence to the evaluation of dental plaque distribution could enhance diagnostic accuracy and treatment efficiency and accuracy.

A Semi-Supervised Transformer-Based Deep Learning Framework for Automated Tooth Segmentation and Identification on Panoramic Radiographs.

Automated tooth segmentation and identification on dental radiographs are crucial steps in establishing digital dental workflows. While deep learning networks have been developed for these tasks, their performance has been inferior in partially edentulous individuals. This study proposes a novel semi-supervised Transformer-based framework (SemiTNet), specifically designed to improve tooth segmentation and identification performance on panoramic radiographs, particularly in partially edentulous cases, and establish an open-source dataset to serve as a unified benchmark. A total of 16,317 panoramic radiographs (1589 labeled and 14,728 unlabeled images) were collected from various datasets to create a large-scale dataset (TSI15k). The labeled images were divided into training and test sets at a 7:1 ratio, while the unlabeled images were used for semi-supervised learning. The SemiTNet was developed using a semi-supervised learning method with a label-guided teacher-student knowledge distillation strategy, incorporating a Transformer-based architecture. The performance of SemiTNet was evaluated on the test set using the intersection over union (IoU), Dice coefficient, precision, recall, and F1 score, and compared with five state-of-the-art networks. Paired t-tests were performed to compare the evaluation metrics between SemiTNet and the other networks. SemiTNet outperformed other networks, achieving the highest accuracy for tooth segmentation and identification, while requiring minimal model size. SemiTNet's performance was near-perfect for fully dentate individuals (all metrics over 99.69%) and excellent for partially edentulous individuals (all metrics over 93%). In edentulous cases, SemiTNet obtained statistically significantly higher tooth identification performance than all other networks. The proposed SemiTNet outperformed previous high-complexity, state-of-the-art networks, particularly in partially edentulous cases. The established open-source TSI15k dataset could serve as a unified benchmark for future studies.

Accuracy of Tooth Segmentation in the Digital Kesling Setup of Two Different Software Programs: A Retrospective Study.

Introduction Precise virtual setup creation and orthodontic appliance fabrication depend on accurate teeth segmentation from intraoral scans. This accuracy is also fundamental for successful orthodontic treatment, as it ensures correct diagnosis and optimal treatment planning. A number of software packages that facilitate the building of virtual setups have been made available in recent years. The performance of these software packages on automatic tooth segmentation has not been widely studied. Hence, the aim of this study was to evaluate the accuracy of automated teeth segmentation in the digital Keslingsetup of Ortho Studio (Maestro 3D Dental Studio, Bordeaux, France) and OrthoAnalyzer (3Shape, Copenhagen, Denmark) software systems. Materials and methods All the scans were taken from the same intraoral scanner (Runyes 3D intraoral scanner; Runyes Medical, Ningbo, China). The scans were stored and imported as stereolithography (STL) files into the Maestro Ortho Studio and 3Shape OrthoAnalyzer software systems. Subsequently, the digital photos underwent alignment in both software applications, an essential stage in each respective workflow prior to any further processing. The digitized images were automatically segmented in Maestro and 3Shape software by a single researcher. For each software interface, the accuracy of teeth segmentation was assessed. An independent t-test, with a significance level set at p < 0.05, was used to evaluate the statistical significance between the two software segmentations. Results The total number of teeth segmented by both software programs utilizing the 12 intraoral scans was 336 for both groups. Successful identification of the tooth segments was 98.21% (n = 330) for 3Shape software and 98.8% (n = 332) for Maestro software. There was no significant difference in the accuracy of determining the tooth segmentations between anterior and posterior teeth, respectively, between both groups, with a p-value of 0.523. Conclusion There were no statistically significant differences between the two software programs, and both demonstrated high success rates for auto-tooth segmentation. Although both programs had excellent success rates, Maestro 3D performed more accurately than 3Shape OrthoAnalyzer.

Euler-angle norms for tooth rotation, torque and tip.

The aim of the current study was to develop and describe a new measuring system for the orientation of a tooth in a digitalized cast of a jaw and provide new angular values for the rotation, torque and tip of maxillary and mandibular teeth. This retrospective cross-sectional study involved the utilization of a sub-group of extrinsic Euler-angles to derive optimal norm values per tooth in three different planes of orientation ('rotation', 'torque' and 'tip') by evaluating the digital representations of the teeth derived from a database containing over 17,500 patients. The process involved the entry of the .stl files of the jaw pairs into a fully automated software system (Smyl:Ai, Ulm, Germany) whereupon jaw alignment, teeth segmentation, landmark identification and visual validation of input files was conducted prior to calculation of the norm values for the three different planes of orientation. The digital scans in stereolithography (.STL)-file format of the upper and lower dentitions of 1914 individuals with optimal occlusion were chosen and evaluated. New mean (standard deviation) angular values were determined for the rotation, torque and tip of maxillary and mandibular teeth. The findings facilitate the reappraisal of rotation, torque and tip values currently acceptable as ideal. They will inform anthropologists and dental researchers about occlusion and alignment in orthodontic and non-orthodontic patients and provide baseline data for future studies. The methodology will also enable the evaluation of large numbers of data in relatively short timeframes.

Deep Learning Based Teeth Segmentation

Deep Learning Based Teeth Segmentation