Identification Of Teeth Research Articles

Metachronous supernumerary teeth in a non-syndromic patient

This paper presents a case of metachronous supernumerary teeth in the mandibular premolar region, following previous removal of a supernumerary tooth and orthodontic treatment, in an adolescent patient following an interval of eight years. Individual or multiple supernumerary teeth can develop anywhere in the jaws, although it is unusual for sequential supernumerary teeth to develop in late adolescence and after orthodontic treatment, particularly in non-syndromic patients. The management of asymptomatic supernumerary teeth should fully consider the risks and benefits of surgical removal. Clinicians should remain aware of the possibility of subsequent supernumerary removal, and the presented case emphasises the importance of radiographic monitoring if subsequent orthodontic treatment is being considered following previous identification of supernumerary teeth.

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.

Integrating Structured Digital Tools with the Traditional Hands-on Puzzle Method for Teaching Tooth Morphology: A Comparative Study of Educational Outcomes.

The study of tooth morphology is a critical component of the dental curriculum, highlighting the importance for dental students to acquire comprehensive and detailed knowledge of the complex structure of teeth. This study compared the educational outcomes of two student cohorts in a tooth morphology course, using traditional methods for the control group and additional digital video-based resources for the experimental group. We hypothesized that early integration of digital resources would significantly reduce the learning time. We retrospectively analyzed two groups of Master of Dentistry students. The control group (42 students) was taught using the traditional 'tooth puzzle' method, while the experimental group (42 students) supplemented traditional teaching with digital video-based tools developed by our department. Both groups' curricula culminated in a practical post-course test requiring the identification of 40 teeth, along with a mid-course test to track the students' learning progression. The number and type of incorrectly identified teeth were recorded. The mid-course test showed significant performance differences. The control group had a median (Q1, Q3) value of faults of 12.0 (7.8, 20.5), whereas the respective value for the experimental group was 4.0 (0.0, 8.0) (p < 0.001). In the control group, none achieved faultless results, with only two students (4.8%) having at most two faults, and six students (14.3%) having no more than four faults. The control group averaged 13.5 faults per student, with 19 students (45.2%) failing the test. Conversely, the experimental group showed improved performance: 12 students (28.6%) had no faults, and 25 students (59.5%) had four or fewer faults. The experimental group averaged 5.2 faults per student, with only four students (9.5%) failing. By the end of the course, both groups achieved commendable results on the practical tooth identification test. The experimental group slightly outperformed the control group, though the difference was not significant. The median (Q1, Q3) values were 0.0 (0.0, 2.5) and 1.0 (0.0, 4.5) for the experimental and control groups, respectively (p = 0.372). The students using both traditional and structured digital video-based tools showed greater learning advancement than those using only the traditional 'tooth puzzle' method.

Automated Analysis of Tooth Anatomy and Pathological Conditions from Orthopantomogram using Deep Neural Networks

This research project aims to automate the identification, labeling, and counting of teeth, as well as the classification of abnormalities and detection of caries in dental X-rays, specifically orthopantomograms (OPGs). It involves several deep neural networks and learning algorithms. The first module uses semantic segmentation with a U-net model to create masks for tooth detection, which are then refined with the YOLOv3 detector, achieving 80% accuracy. Canonical correlation analysis (CCA) helps find tooth midpoints and count the total number of teeth. The second module classifies abnormalities and pathologies using transfer learning with the Inceptionv3 model, yielding moderate accuracy. Caries detection is performed with thresholding and segmentation. The third module detects three treated pathologies—root canal treatments, crowns, and implants—using Faster RCNN and Inceptionv3, showing fair accuracy. Overall, the automated approach demonstrates promising results for enhancing X-ray image interpretation and diagnosing oral diseases.

YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition

ObjectivesIn the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs.MethodsA total of 3854 mixed pediatric patients PRs were labelled for deciduous and permanent teeth using the CranioCatch labeling program. The dataset was divided into three subsets: training (n = 3093, 80% of the total), validation (n = 387, 10% of the total) and test (n = 385, 10% of the total). An artificial intelligence (AI) algorithm using YOLO-v5 models were developed.ResultsThe sensitivity, precision, F-1 score, and mean average precision-0.5 (mAP-0.5) values were 0.99, 0.99, 0.99, and 0.98 respectively, to teeth detection. The sensitivity, precision, F-1 score, and mAP-0.5 values were 0.98, 0.98, 0.98, and 0.98, respectively, to teeth segmentation.ConclusionsYOLO-v5 based models can have the potential to detect and enable the accurate segmentation of deciduous and permanent teeth using PRs of pediatric patients with mixed dentition.

MRCM‐UCTransNet: Automatic and Accurate 3D Tooth Segmentation Network From Cone‐Beam CT Images

ABSTRACTMany scenarios in dental clinical diagnosis and treatment require the segmentation and identification of a specific tooth or the entire dentition in cone‐beam computed tomography (CBCT) images. However, traditional segmentation methods struggle to ensure accuracy. In recent years, there has been significant progress in segmentation algorithms based on deep learning, garnering considerable attention. Inspired by models from present neuro networks such as UCTransNet and DC‐Unet, this study proposes an MRCM‐UCTransNet for accurate three‐dimensional tooth segmentation from cone‐beam CT images. To enhance feature extraction while preserving the multi‐head attention mechanism, a multi‐scale residual convolution module (MRCM) is integrated into the UCTransNet architecture. This modification addresses the limitations of traditional segmentation methods and aims to improve accuracy in tooth segmentation from CBCT images. Comparative experiments indicate that, in the situation with a specific image size and small data volume, the proposed method exhibits certain advantages in segmentation accuracy and precision. Compared to traditional Unet approaches, MRCM‐UCTransNet's dice accuracy is improved by 7%, while its sensitivity is improved by about 10%. These findings highlight the efficacy of the proposed approach, particularly in scenarios with specific image size constraints and limited data availability. The proposed MRCM‐UCTransNet algorithm integrates the latest architectural advancements in the Unet model which achieves effective segmentation of six types of teeth within the tooth. It was proved to be efficient for image segmentation on small datasets, requiring less training time and fewer parameters.

Application of deep learning in isolated tooth identification

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

Assessment of an online tooth morphology course and 3D examination tool during the COVID-19 pandemic.

The COVID-19 pandemic brought major disruptions to dental teaching and has impacted the delivery of tooth morphology courses where students are introduced to the three-dimensional features of the dentition. The aim of this study was to assess the implementation of newly developed online teaching modalities for tooth morphology, evaluate their usefulness and identify elements that are beneficial for learners. Following the delivery of an online course that included online 3D models, 2D cue cards, live discussion sessions and Socrative™ quizzes, the participants were asked to rate the usability and usefulness of each tool. The participants' knowledge of tooth morphology was assessed through an online examination using 3D-digitised tooth models. The participants identified lecture handouts and online 3D models as their preferred learning tools, while lecture video recordings and 2D cue cards were viewed as less useful. Data analysis from Socrative™ quizzes demonstrated improvement in tooth identification skills throughout the course delivery. Finally, results from the final assessment are in line with previous in-person deliveries of this course. The study provides valuable information on the usefulness of teaching modalities that were implemented in response to the COVID-19 pandemic and their merit to be retained in future deliveries of the course. The 3D models have been identified as particularly useful in this context, but the participants still value the opportunity to learn with extracted teeth. Furthermore, it remains to be confirmed whether tooth identification skills acquired using 3D models can be transferred to the clinical setting.

Teeth segmentation and carious lesions segmentation in panoramic X-ray images using CariSeg, a networks' ensemble

BackgroundDental cavities are common oral diseases that can lead to pain, discomfort, and eventually, tooth loss. Early detection and treatment of cavities can prevent these negative consequences. We propose CariSeg, an intelligent system composed of four neural networks that result in the detection of cavities in dental X-rays with 99.42% accuracy. MethodThe first model of CariSeg, trained using the U-Net architecture, segments the area of interest, the teeth, and crops the radiograph around it. The next component segments the carious lesions and it is an ensemble composed of three architectures: U-Net, Feature Pyramid Network, and DeeplabV3. For tooth identification two merged datasets were used: The Tufts Dental Database consisting of 1000 panoramic radiography images and another dataset of 116 anonymized panoramic X-rays, taken at Noor Medical Imaging Center, Qom. For carious lesion segmentation, a dataset consisting of 150 panoramic X-ray images was acquired from the Department of Oral and Maxillofacial Surgery and Radiology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca. ResultsThe experiments demonstrate that our approach results in 99.42% accuracy and a mean 68.2% Dice coefficient. ConclusionsAI helps in detecting carious lesions by analyzing dental X-rays and identifying cavities that might be missed by human observers, leading to earlier detection and treatment of cavities and resulting in better oral health outcomes.

How well do parents identify their child's baby teeth? Engagement and accuracy of parent-reported information on a tooth checklist survey.

Naturally exfoliated primary teeth are being increasingly collected in child development studies. Most of these odontological collections and tooth biobanks use parent-reported information from questionnaires or tooth checklists to collect data on offspring teeth. To the best of the authors' knowledge, no studies have assessed parental engagement in tooth checklists, nor parental accuracy in identifying their child's baby tooth. This study aimed to evaluate these dimensions by analysing data from the about this tooth checklist returned with donated primary teeth in a natural experimental study called STRONG (the Stories Teeth Record of Newborn Growth). Parental self-reported information were analysed on checklists returned with 825 primary teeth belonging to 199 children. The percentage of blank answers was calculated for each question. The accuracy of parents-reported tooth identification was evaluated by comparing parental ratings to researchers' ratings. Reliability of researchers' tooth identification was first evaluated by calculating intra-observer and inter-observer agreements, as well as Cohen's Kappa values. The percentage of accuracy of parents' tooth identification (relative to researcher's) was then calculated, and logistic regressions were used to evaluate if time elapsed between when exfoliation occurred and the checklist was completed associated with parental accuracy in tooth identification. Parents returned 98.4% of the checklists and completed 74.9% to 97.7% of the questions. Excellent reliability was demonstrated for researchers' intra- and inter-rater tooth identification (agreement percentages >90%; Cohen's Kappa values >.83). Moderate accuracy of parents-reported tooth identifications was found, with parents correctly identifying 49.5% of the donated tooth. Better parental accuracies were highlighted for partial identifications (87.1% of correct jaw, 75.6% of correct tooth type, and 65.8% of correct lateralization). Logistic regressions showed the odds of correct parental identifications decreased on average by 1.8% every 30 days of distance between tooth exfoliation and checklist completion. While parental engagement is high, parents-reported tooth identifications have moderate accuracy, which decreases over time. High accuracy is however found for partial identifications. Parent-reported information on the accompanying questionnaire of naturally exfoliated primary teeth collection or tooth biobanks, even when filled in a long time after exfoliation took place, should be encouraged. However, expert identifications of teeth should remain best practice.

Deep learning methods for fully automated dental age estimation on orthopantomograms.

This study aimed to use all permanent teeth as the target and establish an automated dental age estimation method across all developmental stages of permanent teeth, accomplishing all the essential steps of tooth determination, tooth development staging, and dental age assessment. A three-step framework for automatically estimating dental age was developed for children aged 3 to 15. First, a YOLOv3 network was employed to complete the tasks of tooth localization and numbering on a digital orthopantomogram. Second, a novel network named SOS-Net was established for accurate tooth development staging based on a modified Demirjian method. Finally, the dental age assessment procedure was carried out through a single-group meta-analysis utilizing the statistical data derived from our reference dataset. The performance tests showed that the one-stage YOLOv3 detection network attained an overall mean average precision 50 of 97.50 for tooth determination. The proposed SOS-Net method achieved an average tooth development staging accuracy of 82.97% for a full dentition. The dental age assessment validation test yielded an MAE of 0.72years with a full dentition (excluding the third molars) as its input. The proposed automated framework enhances the dental age estimation process in a fast and standard manner, enabling the reference of any accessible population. The tooth development staging network can facilitate the precise identification of permanent teeth with abnormal growth, improving the effectiveness and comprehensiveness of dental diagnoses using pediatric orthopantomograms.

Tooth numbering and classification on bitewing radiographs: an artificial intelligence pilot study

ObjectiveThe aim of this study is to assess the efficacy of employing a deep learning methodology for the automated identification and enumeration of permanent teeth in bitewing radiographs. The experimental procedures and techniques employed in this study are described in the following section. Study DesignA total of 1248 bitewing radiography images were annotated using the CranioCatch labeling program, developed in Eskişehir, Turkey. The dataset has been partitioned into 3 subsets: training (n = 1000, 80% of the total), validation (n = 124, 10% of the total), and test (n = 124, 10% of the total) sets. The images were subjected to a 3 × 3 clash operation in order to enhance the clarity of the labeled regions. ResultsThe F1, sensitivity and precision results of the artificial intelligence model obtained using the Yolov5 architecture in the test dataset were found to be 0.9913, 0.9954, and 0.9873, respectively. ConclusionThe utilization of numerical identification for teeth within deep learning-based artificial intelligence algorithms applied to bitewing radiographs has demonstrated notable efficacy. The utilization of clinical decision support system software, which is augmented by artificial intelligence, has the potential to enhance the efficiency and effectiveness of dental practitioners.

Artificial intelligence and dental panoramic radiographs: where are we now?

Bielefeld Academic Search Engine (BASE), Google Scholar Association for Computing Machinery: Guide to Computing Literature (ACM) and National Library of Medicine: PubMed databases were searched for systematic reviews. This study addressed a structured PICO question (Population, Intervention, Comparison, Outcome). Population was panoramic radiographs in human subjects. Intervention was use of artificial intelligence (AI) diagnostics, compared to human-only diagnosis. Quantitative or qualitative AI efficiency was the outcome. Systematic reviews were considered if they stated 'systematic review' in their title or abstract, were published in English and were not bound by a certain time frame. No supplemental primary studies were included. Screening and removal of duplicates were performed using the Rayyan tool. Data were extracted from each systematic review by two authors, with a third author having the deciding vote in cases of inconsistency. Cohen's Kappa co-efficient was used to measure reliability between authors, resulting in almost perfect agreement. The risk of bias was accounted for using the ROBIS method which resulted in one paper being rejected, so only 11 included in results. Data were then grouped into seven domains which were detected by AI: teeth identification and numbering, detection of periapical lesions, periodontal bone loss, osteoporosis, maxillary sinusitis, dental caries, and other tasks. The effectiveness of the AI systems was assessed by various outcome metrics - accuracy, sensitivity, specificity, and precision being the most common variables. Results of this overview show a significant increase in accuracy of AI in analysing OPTs between 1988-2023. Latest AI models are most accurate in teeth identification and numbering (93.67%) whilst caries detection and osteoporosis showed 91.5% and 89.29% accuracy, respectively. Accurate results were also observed for the detection of maxillary sinusitis and periodontal bone loss. However, given the heterogeneity of source studies used in these systematic reviews, results should be interpreted with caution. With improving AI technology, its use in dental radiology can be increasingly effective in supporting dentists in the detection of different pathologies. This overview has shown that systematic reviews of AI can quickly become outdated and that results of any systematic review should be treated with caution as this field advances. As such, regular updating and ongoing research is required.

Identification of Multiple Faulty Teeth in the Sun Gear Based on the Phenomenological Strain Model and Strain Separation

Identification of Multiple Faulty Teeth in the Sun Gear Based on the Phenomenological Strain Model and Strain Separation

Artificial Intelligence for Caries and Tooth Detection in Dental Imaging: A Review

This review delves into the application of artificial intelligence (AI) and deep learning, particularly leveraging convolutional neural networks (CNNs), to enhance dental diagnostics and treatment planning. The primary focus is on the detection and classification of caries as well as the identification of teeth in diverse dental images. A thorough exploration was conducted across databases, including PubMed, IEEE Xplore, and arXiv.org, leading to the identification of 29 pertinent studies. These studies employ various neural network models, encompassing different dental image types and employing diverse performance metrics. The review succinctly outlines the key characteristics and outcomes of these studies, underscoring the remarkable accuracy and the promising potential of AI-driven approaches in the realms of caries detection and tooth identification.&#x0D; Acknowledging the existing limitations within the current body of research, such as small or non-representative datasets, variations in imaging techniques, and a lack of interpretability in deep learning models, the review emphasizes the need for future investigations. It suggests potential research directions aimed at overcoming these challenges, thereby facilitating the seamless integration of AI into routine dental practices.

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

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. 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. 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%. 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.

Deep learning for tooth identification and enumeration in panoramic radiographs

Deep learning for tooth identification and enumeration in panoramic radiographs

A full-set tooth segmentation model based on improved PointNet++

Segmentation of a complete set of teeth from three-dimensional (3D) intra-oral scanner images is a crucial step in tooth identification procedures. In large-scale disasters with many victims, teeth are often the preferred and reliable source for victim identification due to their hard and non-deformable characteristics. In this paper we present a study on the automatic segmentation of a complete set of teeth from intra-oral scanner images. We propose a tooth segmentation method based on an improved PointNet++ architecture. To address the problem of inadequate segmentation capability of the teeth-gingival boundary of PointNet++, we introduce a single-point preliminary feature extraction (SPFE) module to better preserve the subtle details that may be overlooked by the original PointNet++ model. In addition, a weighted-sum local feature aggregation (WSLFA) mechanism is proposed to replace the max pooling in PointNet++ to better perform feature aggregation. The experimental results on 52 testing datasets using the network trained on 160 annotated 3D intra-oral scanner images demonstrate that our improved PointNet++ method achieves a segmentation accuracy of 97.68%, and performs well under different dental conditions.

Development and validation of an artificial intelligence software for periodontal bone loss in panoramic imaging

AbstractThis retrospective study is aimed at developing a web‐based artificial intelligence (AI) software (DiagnoCat) for periodontal bone loss detection on panoramic radiographs and evaluating the model's performance by comparing it with clinicians' results. Separate models are trained for tooth and periodontal bone loss detection. The first model's objective was to detect teeth, segmenting their masks, and to define their numbering and developed with Mask R‐CNN using pretrained ResNet‐101 as a backbone. The second model was based on Cascade R‐CNN architecture and used for bone loss prediction. Around 100 radiographs are evaluated by three clinicians regarding tooth identification and periodontal bone loss, separately. Ground truth is determined by the consensus and model's performance is evaluated with kappa, precision, recall, and F‐score statistics. For tooth conditions, the overall F‐score, accuracy, and Cohen's kappa coefficients were found to be 0.948, 0.977, and 0.933 for the binary, and 0.992, 0.988, and 0.961 for the multiclass results. For bone loss detection, the overall F‐score, accuracy, and Cohen's kappa coefficients were found to be 0.985, 0.980, and 0.956 for the binary, and 0.996, 0.993, and 0.974 for the multiclass results. The results of this study suggest that the use of a web‐based AI software (DiagnoCat) can be beneficial in detecting periodontal bone loss on panoramic radiographs.

The implication of instructional design for deciduous tooth identification in a dental morphology course for undergraduate dental students

Background/purposeThe pediatric dentistry courses are lacking in our six-year undergraduate dental education. The purpose of this study was to evaluate the implication of instructional design for deciduous tooth identification in a dental morphology course for undergraduate dental students through students’ perspectives. Materials and methodsA total of 34 s-year dental students who took this dental morphology course were invited to fill out the questionnaire for survey of instructional design for deciduous tooth identification after the class. ResultsOf the 34 dental students, 32 of them participated in the survey with a valid response rate of 94.1%. The results showed that most students found the learning activity of instructional design for deciduous tooth identification to be helpful for improving their knowledge about deciduous dentition. Most of them also had positive attitude towards this instructional design. ConclusionSince the proportion of pediatric dentistry courses in the undergraduate dental education is very low and children's oral problems are indeed faced by all dentists, it is important to add learning units of pediatric dentistry-related knowledge in different undergraduate dental professional courses or to propose strategies to promote students' awareness of self-directed learning about pediatric dentistry. This study may serve as a model for other undergraduate dental courses in Taiwan.