Tooth replacement in the early-diverging neornithischian Jeholosaurus shangyuanensis and implications for dental evolution and herbivorous adaptation in Ornithischia.

BACKGROUND: Tooth replacement patterns of early-diverging ornithischians, which are important for understanding the evolution of the highly specialized dental systems in hadrosaurid and ceratopsid dinosaurs, are poorly known. The early-diverging neornithischian Jeholosaurus, a small, bipedal herbivorous dinosaur from the Early Cretaceous Jehol Biota, is an important taxon for understanding ornithischian dental evolution, but its dental morphology was only briefly described previously and its tooth replacement is poorly known. RESULTS: CT scanning of six specimens representing different ontogenetic stages of Jeholosaurus reveals significant new information regarding the dental system of Jeholosaurus, including one or two replacement teeth in nearly all alveoli, relatively complete tooth resorption, and an increase in the numbers of alveoli and replacement teeth during ontogeny. Reconstructions of Zahnreihen indicate that the replacement pattern of the maxillary dentition is similar to that of the dentary dentition but with a cyclical difference. The maxillary tooth replacement rate in Jeholosaurus is probably 46 days, which is faster than that of most other early-diverging ornithischians. During the ontogeny of Jeholosaurus, the premaxillary tooth replacement rate slows from 25 days to 33 days with similar daily dentine formation. CONCLUSIONS: The tooth replacement rate exhibits a decreasing trend with ontogeny, as in Alligator. In a phylogenetic context, fast tooth replacement and multi-generation replacement teeth have evolved at least twice independently in Ornithopoda, and our analyses suggest that the early-diverging members of the major ornithischian clades exhibit different tooth replacement patterns as an adaption to herbivory.

Performance comparison of multifarious deep networks on caries detection with tooth X-ray images.

OBJECTIVES: Deep networks have been preliminarily studied in caries diagnosis based on clinical X-ray images. However, the performance of different deep networks on caries detection is still unclear. This study aims to comprehensively compare the caries detection performances of recent multifarious deep networks with clinical dentist level as a bridge. METHODS: Based on the self-collected periapical radiograph dataset in clinic, four most popular deep networks in two types, namely YOLOv5 and DETR object detection networks, and UNet and Trans-UNet segmentation networks, were included in the comparison study. Five dentists carried out the caries detection on the same testing dataset for reference. Key tooth-level metrics, including precision, sensitivity, specificity, F1-score and Youden index, were obtained, based on which statistical analysis was conducted. RESULTS: The F1-score order of deep networks is YOLOv5 (0.87), Trans-UNet (0.86), DETR (0.82) and UNet (0.80) in caries detection. A same ranking order is found using the Youden index combining sensitivity and specificity, which are 0.76, 0.73, 0.69 and 0.64 respectively. A moderate level of concordance was observed between all networks and the gold standard. No significant difference (p > 0.05) was found between deep networks and between the well-trained network and dentists in caries detection. CONCLUSIONS: Among investigated deep networks, YOLOv5 is recommended to be priority for caries detection in terms of its high metrics. The well-trained deep network could be used as a good assistance for dentists to detect and diagnose caries. CLINICAL SIGNIFICANCE: The well-trained deep network shows a promising potential clinical application prospect. It can provide valuable support to healthcare professionals in facilitating detection and diagnosis of dental caries.

Occlusal characteristics in modern humans with tooth agenesis.

Non-syndromic permanent tooth agenesis affects a significant proportion of the population, especially if third molars are considered. Although tooth agenesis has been linked to a smaller craniofacial size, reduced facial convexity and a shorter skeletal face, the occlusal characteristics of individuals with tooth agenesis remain largely unexplored. Therefore, this study investigated potential associations between tooth agenesis and metric occlusal traits in 806 individuals (491 with 4.1 missing teeth per subject, including third molars, and 315 without any tooth agenesis). Dentoskeletal morphology was defined through anatomical landmarks on pre-treatment cephalometric radiographs. Multivariate regression models, adjusted for sex and age, showed that tooth agenesis was significantly associated with a reduced overjet, an increased interincisal angle, and shorter upper and lower dental arch lengths, but not with overbite. Moreover, apart from reduced tooth length and dentoalveolar effects, as the number of missing teeth increased the upper front teeth were progressively retruded according to the craniofacial complex and to the face. Thus, tooth agenesis has a substantial influence on dental and occlusal characteristics, as well as on the sagittal position and inclination of anterior teeth. These findings emphasize the necessity for personalized, multidisciplinary approaches in individuals with multiple agenesis to successfully meet treatment goals.

Sex and ancestry patterning of residual correlations in human dental development: Cooperative genetic interaction and phenotypic plasticity.

OBJECTIVES: Most research in human dental age estimation has focused on point estimates of age, and most research on dental development theories has focused on morphology or eruption. Correlations between developing teeth using ordinal staging have received less attention. The effect of demographic variables on these correlations is unknown. I tested the effect of reference sample demographic variables on the residual correlation matrix using the lens of cooperative genetic interaction (CGI). MATERIALS AND METHODS: The sample consisted of Moorrees et al., Journal of Dental Research, 1963, 42, 1490-1502, scores of left mandibular permanent teeth from panoramic radiographs of 880 London children 3-22.99 years of age stratified by year of age, sex, and Bangladeshi or European ancestry. A multivariate cumulative probit model was fit to each sex/ancestry group (n = 220), each sex or ancestry (n = 440), and all individuals (n = 880). Residual correlation matrices from nine reference sample configurations were compared using Bartlett's tests of between-sample difference matrices against the identity matrix, hierarchical cluster analysis, and dendrogram cophenetic correlations. RESULTS: Bartlett's test results were inconclusive. Cluster analysis showed clustering by tooth class, position within class, and developmental timing. Clustering patterns and dendrogram correlations showed similarity by sex but not ancestry. DISCUSSION: Expectations of CGI were supported for developmental staging. This supports using CGI as a model for explaining patterns of variation within the dentition. Sex was found to produce consistent patterns of dental correlations, whereas ancestry did not. Clustering by timing of development supports phenotypic plasticity in the dentition and suggests shared environment over genetic ancestry to explain population differences.

The repeatability of periodontal imaging with intraoral ultrasound scanning.

OBJECTIVE: Ultrasound is a non-invasive and low-cost diagnostic tool widely used in medicine. Recent studies have demonstrated that ultrasound imaging might have the potential to be used intraorally to assess the periodontium by comparing it to current imaging methods. This study aims to characterize the repeatability of intraoral periodontal ultrasound imaging. MATERIALS AND METHODS: Two hundred and twenty-three teeth were scanned from fourteen volunteers participating in this study. One operator conducted all the scans in each tooth thrice with a 20 MHz intraoral ultrasound. The repeatability of three measurements, alveolar bone crest to the cementoenamel junction (ABC-CEJ), gingival thickness (GT), and alveolar bone thickness (ABT), was calculated with intercorrelation coefficient (ICC). Measurements were also compared with mean absolute deviation (MAD), repeatability coefficient (RC), and descriptive statistics. RESULTS: ICC scores for intra-rater repeatability were 0.917(0.897,0.933), 0.849(0.816,0.878), and 0.790(0.746,0.898), MAD results were 0.610 mm (± 0.508), 0.224 (± 0.200), and 0.067 (± 0.060), and RC results were 0.648, 0.327, and 0.121 for ABC-CEJ, GT, and ABT measurements, respectively. CONCLUSION: Results of the present study pointed towards good or excellent repeatability of ultrasound as a measurement tool for periodontal structures. CLINICAL RELEVANCE: Clinicians could benefit from the introduction of a novel chairside diagnostic tool. Ultrasound is a non-invasive imaging assessment tool for the periodontium with promising results in the literature. Further validation, establishment of scanning protocols, and commercialization are still needed before ultrasound imaging is available for clinicians.

Deep learning-based tooth segmentation methods in medical imaging: A review.

Deep learning approaches for tooth segmentation employ convolutional neural networks (CNNs) or Transformers to derive tooth feature maps from extensive training datasets. Tooth segmentation serves as a critical prerequisite for clinical dental analysis and surgical procedures, enabling dentists to comprehensively assess oral conditions and subsequently diagnose pathologies. Over the past decade, deep learning has experienced significant advancements, with researchers introducing efficient models such as U-Net, Mask R-CNN, and Segmentation Transformer (SETR). Building upon these frameworks, scholars have proposed numerous enhancement and optimization modules to attain superior tooth segmentation performance. This paper discusses the deep learning methods of tooth segmentation on dental panoramic radiographs (DPRs), cone-beam computed tomography (CBCT) images, intro oral scan (IOS) models, and others. Finally, we outline performance-enhancing techniques and suggest potential avenues for ongoing research. Numerous challenges remain, including data annotation and model generalization limitations. This paper offers insights for future tooth segmentation studies, potentially facilitating broader clinical adoption.

Performance evaluation of three versions of a convolutional neural network for object detection and segmentation using a multiclass and reduced panoramic radiograph dataset.

OBJECTIVES: To evaluate the diagnostic performance of three versions of a deep-learning convolutional neural network in terms of object detection and segmentation using a multiclass panoramic radiograph dataset. METHODS: A total of 600 orthopantomographies were randomly selected for this study and manually annotated by a single operator using an image annotation tool (COCO Annotator v.11.0.1) to establish ground truth. The annotation classes included teeth, maxilla, mandible, inferior alveolar nerve, dento- and implant-supported crowns/pontics, endodontic treatment, resin-based restorations, metallic restorations, and implants. The dataset was then divided into training, validation, and testing subsets, which were used to train versions 5, 7, and 8 of You Only Look Once (YOLO) Neural Network. Results were stored, and a posterior performance analysis was carried out by calculating the precision (P), recall (R), F1 Score, Intersection over Union (IoU), and mean average precision (mAP) at 0.5 and 0.5-0.95 thresholds. The confusion matrix and recall precision graphs were also sketched. RESULTS: YOLOv5s showed an improvement in object detection results with an average R = 0.634, P = 0.781, mAP0.5 = 0.631, and mAP0.5-0.95 = 0.392. YOLOv7m achieved the best object detection results with average R = 0.793, P = 0.779, mAP0.5 = 0.740, and mAP0.5-0.95 = 0,481. For object segmentation, YOLOv8m obtained the best average results (R = 0.589, P = 0.755, mAP0.5 = 0.591, and mAP0.5-0.95 = 0.272). CONCLUSIONS: YOLOv7m was better suited for object detection, while YOLOv8m demonstrated superior performance in object segmentation. The most frequent error in object detection was related to background classification. Conversely, in object segmentation, there is a tendency to misclassify True Positives across different dental treatment categories. CLINICAL SIGNIFICANCE: General diagnostic and treatment decisions based on panoramic radiographs can be enhanced using new artificial intelligence-based tools. Nevertheless, the reliability of these neural networks should be subjected to training and validation to ensure their generalizability.

Windows into the past: recent scientific techniques in dental analysis.

Teeth are the hardest and most chemically stable tissues in the body, are well-preserved in archaeological remains and, being resistant to decomposition in the soil, survive long after their supporting structures have deteriorated. It has long been recognised that visual and radiographic examination of teeth can provide considerable information relating to the lifestyle of an individual. This paper examines the latest scientific approaches that have become available to investigate recent and ancient teeth. These techniques include DNA analysis, which can be used to determine the sex of an individual, indicate familial relationships, study population movements, provide phylogenetic information and identify the presence of disease pathogens. A stable isotopic approach can shed light on aspects of diet and mobility and even research climate change. Proteomic analysis of ancient dental calculus can reveal specific information about individual diets. Synchrotron microcomputed tomography is a non-invasive technique which can be used to visualise physiological impactful events, such as parturition, menopause and diseases in cementum microstructure - these being displayed as aberrant growth lines.

Quantitative photothermal analysis and multispectral imaging of dental structures: insights into optical and thermal properties of carious and healthy teeth.

Significance: In the analysis of two-layered turbid dental tissues, the outer finite-thickness layer is modeled by an optical transport coefficient distinct from its underlying semi-infinite substrate layer. The optical and thermophysical parameters of healthy and carious teeth across the various wavelengths were measured leading to the determination of the degree of reliability of each of the fitted parameters, with most reliable being thermal diffusivity and conductivity, enamel thickness, and optical transport coefficient of the enamel layer. Quantitative pixel-by-pixel images of the key reliable optical and thermophysical parameters were constructed. Aim: We introduced a theoretical model of pulsed photothermal radiometry based on conduction-radiation theory and applied to quantitative photothermal detection and imaging of biomaterials. The theoretical model integrates a combination of inverse Fourier transformation techniques, avoiding the conventional cumbersome analytical Laplace transform method. Approach: Two dental samples were selected for analysis: the first sample featured controlled, artificially induced early caries on a healthy tooth surface, while the second sample exhibited natural defects along with an internal filling. Using an Nd:YAG laser and specific optical parametric oscillator (OPO) wavelengths (675, 700, 750, and 808 nm), photothermal transient signals were captured from different points on these teeth and analyzed as a function of OPO wavelength. Measurements were also performed with an 808-nm laser diode for comparison with the same OPO wavelength excitation, particularly for the second sample with natural defects. Results: The findings demonstrated that the photothermal transient signals exhibit a fast-decaying pattern at shorter wavelengths due to their higher scattering nature, while increased scattering and absorption in the carious regions masked conductive and radiative contributions from the underlayer. These observations were cross-validated using micro-computed tomography, which also enabled the examination of signal patterns at different tooth locations. Conclusions: The results of our study showed the impact of optical and thermal characteristics of two-layered turbid dental tissues via an inverse Fourier technique, as well as the interactions between these layers, on the patterns observed in depth profiles.

Deep learning for tooth identification and numbering on dental radiography: a systematic review and meta-analysis.

OBJECTIVES: Improved tools based on deep learning can be used to accurately number and identify teeth. This study aims to review the use of deep learning in tooth numbering and identification. METHODS: An electronic search was performed through October 2023 on PubMed, Scopus, Cochrane, Google Scholar, IEEE, arXiv, and medRxiv. Studies that used deep learning models with segmentation, object detection, or classification tasks for teeth identification and numbering of human dental radiographs were included. For risk of bias assessment, included studies were critically analysed using quality assessment of diagnostic accuracy studies (QUADAS-2). To generate plots for meta-analysis, MetaDiSc and STATA 17 (StataCorp LP, College Station, TX, USA) were used. Pooled outcome diagnostic odds ratios (DORs) were determined through calculation. RESULTS: The initial search yielded 1618 studies, of which 29 were eligible based on the inclusion criteria. Five studies were found to have low bias across all domains of the QUADAS-2 tool. Deep learning has been reported to have an accuracy range of 81.8%-99% in tooth identification and numbering and a precision range of 84.5%-99.94%. Furthermore, sensitivity was reported as 82.7%-98% and F1-scores ranged from 87% to 98%. Sensitivity was 75.5%-98% and specificity was 79.9%-99%. Only 6 studies found the deep learning model to be less than 90% accurate. The average DOR of the pooled data set was 1612, the sensitivity was 89%, the specificity was 99%, and the area under the curve was 96%. CONCLUSION: Deep learning models successfully can detect, identify, and number teeth on dental radiographs. Deep learning-powered tooth numbering systems can enhance complex automated processes, such as accurately reporting which teeth have caries, thus aiding clinicians in making informed decisions during clinical practice.

A 3-year follow-up clinical study on the preservation for vitality of involved tooth in jaw cysts through an innovative method.

Jaw cysts commonly affect the oral and maxillofacial region, involving adjacent tooth roots. The management of these teeth, particularly regarding root canal therapy and apicoectomy, lacks consensus. This study introduces a novel treatment concept and refined surgical approach to preserve pulp viability in teeth involved in jaw cysts. The objective was to investigate the effectiveness and potential benefits of this approach over a 36-month follow-up period. A conservative management approach prioritized vitality preservation, reserving root canal treatment and apicectomy for cases with post-operative discomfort. A comprehensive follow-up of 108 involved teeth from 36 jaw cyst cases treated with the modified method was conducted. Clinical observation, X-ray imaging, cone-beam computed tomography (CBCT), and pulp vitality testing assessed changes in cyst size, tooth color, pulp vitality, root structure, and surrounding alveolar bone. After 36 months, our modified surgical approach successfully preserved tooth vitality in 84 involved teeth. Adverse symptoms in 19 teeth, such as redness, swelling, fistula, and pain, resolved with postoperative root canal therapy. Follow-up was lost for five teeth in two cases. No cyst recurrences were observed, and in 34 cases, the bone cavity gradually disappeared, restoring normal bone density during long-term follow-up. Our modified surgical method effectively preserves tooth vitality in jaw cysts. This innovative approach has the potential to improve the management of teeth involved in jaw cysts.

Mask refinement network for tooth segmentation on panoramic radiographs.

OBJECTIVES: Instance-level tooth segmentation extracts abundant localization and shape information from panoramic radiographs (PRs). The aim of this study was to evaluate the performance of a mask refinement network that extracts precise tooth edges. METHODS: A public dataset which consists of 543 PRs and 16211 labelled teeth was utilized. The structure of a typical Mask Region-based Convolutional Neural Network (Mask RCNN) was used as the baseline. A novel loss function was designed focus on producing accurate mask edges. In addition to our proposed method, 3 existing tooth segmentation methods were also implemented on the dataset for comparative analysis. The average precisions (APs), mean intersection over union (mIoU), and mean Hausdorff distance (mHAU) were exploited to evaluate the performance of the network. RESULTS: A novel mask refinement region-based convolutional neural network was designed based on Mask RCNN architecture to extract refined masks for individual tooth on PRs. A total of 3311 teeth were correctly detected from 3382 tested teeth in 111 PRs. The AP, precision, and recall were 0.686, 0.979, and 0.952, respectively. Moreover, the mIoU and mHAU achieved 0.941 and 9.7, respectively, which are significantly better than the other existing segmentation methods. CONCLUSIONS: This study proposed an efficient deep learning algorithm for accurately extracting the mask of any individual tooth from PRs. Precise tooth masks can provide valuable reference for clinical diagnosis and treatment. This algorithm is a fundamental basis for further automated processing applications.

Responsiveness of three measurements in cone-beam computed tomography transverse analyses during both tooth-supported and mini-screw-assisted rapid maxillary expansion.

OBJECTIVE: To evaluate the responsiveness of three cone-beam computed tomography (CBCT) transverse analyses (University of the Pennsylvania [UPenn] analysis, Boston University analysis and Yonsei University [YU] analysis). MATERIALS AND METHODS: A consecutive cohort sample of patients was retrospectively reviewed for eligibility. CBCT records before treatment (T0) and immediately after maxillary expansion (T1) of 71 patients receiving tooth-supported rapid maxillary expansion (RME) and 57 patients receiving mini-screw-assisted RME (MARME) were finally analyzed. Responsiveness was assessed by comparing changes of measures (T1-T0) to mid-palatal suture opening distance (MSOD) at T1. Correlational responsiveness was assessed by Pearson correlation coefficient (r). Absolute agreement responsiveness was assessed by Bland-Altman analysis. A specialized intraclass correlation coefficient (ICC) was selected to assess responsiveness combining correlation and absolute agreement. RESULTS: Changes of all three measures were moderately to strongly correlated to MSOD (r > 0.5). The highest correlation coefficient (0.79) was found between the YU analysis and MSOD. When exploring absolute agreement responsiveness, the smallest deviation (0.14 mm) was observed in the UPenn analysis. For ICC, the highest ICC value (0.63) was observed when the YU analysis was used. In addition, all three measurements were more responsive to MSOD in the MARME group than to those in RME group. CONCLUSIONS: All three transverse measurements responded well to true changes of maxillary transverse deficiency during both tooth-supported and mini-screw-assisted RME. Deviations of responsive properties of these measurements from true skeletal changes were below a clinically meaningful level (1 mm).

DTR-Net: Dual-Space 3D Tooth Model Reconstruction From Panoramic X-Ray Images.

In digital dentistry, cone-beam computed tomography (CBCT) can provide complete 3D tooth models, yet suffers from a long concern of requiring excessive radiation dose and higher expense. Therefore, 3D tooth model reconstruction from 2D panoramic X-ray image is more cost-effective, and has attracted great interest in clinical applications. In this paper, we propose a novel dual-space framework, namely DTR-Net, to reconstruct 3D tooth model from 2D panoramic X-ray images in both image and geometric spaces. Specifically, in the image space, we apply a 2D-to-3D generative model to recover intensities of CBCT image, guided by a task-oriented tooth segmentation network in a collaborative training manner. Meanwhile, in the geometric space, we benefit from an implicit function network in the continuous space, learning using points to capture complicated tooth shapes with geometric properties. Experimental results demonstrate that our proposed DTR-Net achieves state-of-the-art performance both quantitatively and qualitatively in 3D tooth model reconstruction, indicating its potential application in dental practice.

Autologous Transplantation Tooth Guide Design Based on Deep Learning.

BACKGROUND: Autologous tooth transplantation requires precise surgical guide design, involving manual tracing of donor tooth contours based on patient cone-beam computed tomography (CBCT) scans. While manual corrections are time-consuming and prone to human errors, deep learning-based approaches show promise in reducing labor and time costs while minimizing errors. However, the application of deep learning techniques in this particular field is yet to be investigated. PURPOSE: We aimed to assess the feasibility of replacing the traditional design pipeline with a deep learning-enabled autologous tooth transplantation guide design pipeline. STUDY DESIGN, SETTING, SAMPLE: This retrospective cross-sectional study used 79 CBCT images collected at the Guangzhou Medical University Hospital between October 2022 and March 2023. Following preprocessing, a total of 5,070 region of interest images were extracted from 79 CBCT images. PREDICTOR VARIABLE: Autologous tooth transplantation guide design pipelines, either based on traditional manual design or deep learning-based design. MAIN OUTCOME VARIABLE: The main outcome variable was the error between the reconstructed model and the gold standard benchmark. We used the third molar extracted clinically as the gold standard and leveraged it as the benchmark for evaluating our reconstructed models from different design pipelines. Both trueness and accuracy were used to evaluate this error. Trueness was assessed using the root mean square (RMS), and accuracy was measured using the standard deviation. The secondary outcome variable was the pipeline efficiency, assessed based on the time cost. Time cost refers to the amount of time required to acquire the third molar model using the pipeline. ANALYSES: Data were analyzed using the Kruskal-Wallis test. Statistical significance was set at P < .05. RESULTS: In the surface matching comparison for different reconstructed models, the deep learning group achieved the lowest RMS value (0.335 ± 0.066 mm). There were no significant differences in RMS values between manual design by a senior doctor and deep learning-based design (P = .688), and the standard deviation values did not differ among the 3 groups (P = .103). The deep learning-based design pipeline (0.017 ± 0.001 minutes) provided a faster assessment compared to the manual design pipeline by both senior (19.676 ± 2.386 minutes) and junior doctors (30.613 ± 6.571 minutes) (P < .001). CONCLUSIONS AND RELEVANCE: The deep learning-based automatic pipeline exhibited similar performance in surgical guide design for autogenous tooth transplantation compared to manual design by senior doctors, and it minimized time costs.

A fine-grained orthodontics segmentation model for 3D intraoral scan data.

With the widespread application of digital orthodontics in the diagnosis and treatment of oral diseases, more and more researchers focus on the accurate segmentation of teeth from intraoral scan data. The accuracy of the segmentation results will directly affect the follow-up diagnosis of dentists. Although the current research on tooth segmentation has achieved promising results, the 3D intraoral scan datasets they use are almost all indirect scans of plaster models, and only contain limited samples of abnormal teeth, so it is difficult to apply them to clinical scenarios under orthodontic treatment. The current issue is the lack of a unified and standardized dataset for analyzing and validating the effectiveness of tooth segmentation. In this work, we focus on deformed teeth segmentation and provide a fine-grained tooth segmentation dataset (3D-IOSSeg). The dataset consists of 3D intraoral scan data from more than 200 patients, with each sample labeled with a fine-grained mesh unit. Meanwhile, 3D-IOSSeg meticulously classified every tooth in the upper and lower jaws. In addition, we propose a fast graph convolutional network for 3D tooth segmentation named Fast-TGCN. In the model, the relationship between adjacent mesh cells is directly established by the naive adjacency matrix to better extract the local geometric features of the tooth. Extensive experiments show that Fast-TGCN can quickly and accurately segment teeth from the mouth with complex structures and outperforms other methods in various evaluation metrics. Moreover, we present the results of multiple classical tooth segmentation methods on this dataset, providing a comprehensive analysis of the field. All code and data will be available at https://github.com/MIVRC/Fast-TGCN.

Tooth pattern, development, and replacement in the yellow catfish, Pelteobagrus fulvidraco.

Studies of teleost teeth are important for understanding the evolution and mechanisms of tooth development, replacement, and regeneration. Here, we used gross specimens, microcomputed tomography, and histological analysis to characterize tooth structure, development, and resorption patterns in adult Pelteobagrus fulvidraco. The oral and pharyngeal teeth are villiform and conical. Multiple rows of dentition are densely distributed and the tooth germ is derived from the epithelium. P. fulvidraco exhibits a discontinuous and non-permanent dental lamina. Epithelial cells surround the teeth and are separated into distinct tooth units by mesenchymal tissue. Tooth development is completed in the form of independent tooth units. P. fulvidraco does not undergo simultaneous tooth replacement. Based on tooth development and resorption status, five forms of teeth are present in adult P. fulvidraco: developing tooth germs, accompanied by relatively immature tooth germs; mature and well-mineralized tooth accompanied by one tooth germ; teeth that have begun resorption, but not completely fractured; fractured teeth with only residual attachment to the underlying bone; and teeth that are completely resorbed and detached. Seven biological stages of a tooth in P. fulvidraco were also described.

A Critical Analysis of the Limitation of Deep Learning based 3D Dental Mesh Segmentation Methods in Segmenting Partial Scans.

Tooth segmentation from intraoral scans is a crucial part of digital dentistry. Many Deep Learning based tooth segmentation algorithms have been developed for this task. In most of the cases, high accuracy has been achieved, although, most of the available tooth segmentation techniques make an implicit restrictive assumption of full jaw model and they report accuracy based on full jaw models. Medically, however, in certain cases, full jaw tooth scan is not required or may not be available. Given this practical issue, it is important to understand the robustness of currently available widely used Deep Learning based tooth segmentation techniques. For this purpose, we applied available segmentation techniques on partial intraoral scans and we discovered that the available deep Learning techniques under-perform drastically. The analysis and comparison presented in this work would help us in understanding the severity of the problem and allow us to develop robust tooth segmentation technique without strong assumption of full jaw model.Clinical relevance- Deep learning based tooth mesh segmentation algorithms have achieved high accuracy. In the clinical setting, robustness of deep learning based methods is of utmost importance. We discovered that the high performing tooth segmentation methods under-perform when segmenting partial intraoral scans. In our current work, we conduct extensive experiments to show the extent of this problem. We also discuss why adding partial scans to the training data of the tooth segmentation models is non-trivial. An in-depth understanding of this problem can help in developing robust tooth segmentation tenichniques.

V2-Net: An Attention-guided Volumetric Regression Network for Tooth Landmark Localization on CT Images with Metal Artifacts.

For virtual surgical planning in orthognathic surgery, marking tooth landmarks on CT images is an important procedure. However, the manual localization procedure of tooth landmarks is time-consuming, labor-intensive, and requires expert knowledge. Also, direct and automatic tooth landmark localization on CT images is difficult because of the lower resolution and metal artifacts of dental images. The purpose of this study was to propose an attention-guided volumetric regression network (V2-Net) for accurate tooth landmark localization on CT images with metal artifacts and lower resolution. V2-Net has an attention-guided network architecture using a coarse-to-fine-attention mechanism that guided the 3D probability distribution of tooth landmark locations within anatomical structures from the coarse V-Net to the fine V-Net for more focus on tooth landmarks. In addition, we combined attention-guided learning and a 3D attention module with optimal Pseudo Huber loss to improve the localization accuracy. Our results show that the proposed method achieves state-of-the-art accuracy of 0.85 ± 0.40 mm in terms of mean radial error, outperforming previous studies. In ablation studies, we observed that the proposed attention-guided learning and a 3D attention module improved the accuracy of tooth landmark localization in CT images of lower resolution and metal artifacts. Furthermore, our method achieved 97.92% in terms of the success detection rate within the clinically accepted accuracy range of 2.0 mm.

Teeth and prostheses detection in dental panoramic X-rays using CNN-based object detector and a priori knowledge-based algorithm.

Deep learning techniques for automatically detecting teeth in dental X-rays have gained popularity, providing valuable assistance to healthcare professionals. However, teeth detection in X-ray images is often hindered by alterations in tooth appearance caused by dental prostheses. To address this challenge, our paper proposes a novel method for teeth detection and numbering in dental panoramic X-rays, leveraging two separate CNN-based object detectors, namely YOLOv7, for detecting teeth and prostheses, alongside an optimization algorithm to refine the outcomes. The study utilizes a dataset of 3138 radiographs, of which 2553 images contain prostheses, to build a robust model. The tooth and prosthesis detection algorithms perform excellently, achieving mean average precisions of 0.982 and 0.983, respectively. Additionally, the trained tooth detection model is verified using an external dataset, and six-fold cross-validation is conducted to demonstrate the proposed method's feasibility and robustness. Moreover, the investigation of performance improvement resulting from the inclusion of prosthesis information in the teeth detection process reveals a marginal increase in the average F1-score, rising from 0.985 to 0.987 compared to the sole teeth detection method. The proposed method is unique in its approach to numbering teeth as it incorporates prosthesis information and considers complete restorations such as dental implants and dentures of fixed bridges during the teeth enumeration process, which follows the universal tooth numbering system. These advancements hold promise for automating dental charting processes.