Materials and Methods: A total of 8,030 intraoral images were retrospectively collected from 3 groups of undergraduate clinical dental students. The type of examination, stage of the procedure, and reasons for repetition were analysed and recorded. The repeat rate was calculated as the total number of repeated images divided by the total number of examinations. The weighted Cohen's kappa for inter- and intra-observer agreement was used after calibration and prior to image analysis.
Results: The overall repeat rate on intraoral periapical images was 34.4%. A total of 1,978 repeated periapical images were from endodontic assessment, which included working length estimation (WLE), trial gutta-percha (tGP), obturation, and removal of gutta-percha (rGP). In the endodontic imaging, the highest repeat rate was from WLE (51.9%) followed by tGP (48.5%), obturation (42.2%), and rGP (35.6%). In bitewing images, the repeat rate was 15.1% and poor angulation was identified as the most common cause of error. A substantial level of intra- and interobserver agreement was achieved.
Conclusion: The repeat rates in this study were relatively high, especially for certain clinical procedures, warranting training in optimization techniques and radiation protection. Repeat analysis should be performed from time to time to enhance quality assurance and hence deliver high-quality health services to patients.
Materials and Methods: Routinely taken lateral cephalograms from 408 subjects aged 10 to 18 years were evaluated retrospectively using the CVM stages described by Baccetti et al. Descriptive statistics, accuracy, sensitivity, specificity, positive and negative predictive values, and likelihood ratios were calculated for stages 2, 3, and 4 of CVM.
Results: Real age increased as the CVM stage gradually increased. The results of 2×2 contingency tables showed that CVM stage 4 produced an accuracy of 71% and 73%, a false positive rate of 7% and 18%, and a post-test probability of 59% and 68% for boys and girls, respectively.
Conclusion: Based on these findings, it can be concluded that the stages of CVM are of limited use for predicting the attainment of the legal age threshold of 14 years. Future studies should investigate whether combinations of skeletal and dental methods could achieve better accuracy and post-test probability.
Materials and Methods: In order to conduct this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Studies published from 2015 to 2021 under the keywords (deep convolutional neural network) AND (caries), (deep learning caries) AND (convolutional neural network) AND (caries) were systematically reviewed.
Results: When dental caries is improperly diagnosed, the lesion may eventually invade the enamel, dentin, and pulp tissue, leading to loss of tooth function. Rapid and precise detection and diagnosis are vital for implementing appropriate prevention and treatment of dental caries. Radiography and intraoral images are considered to play a vital role in detecting dental caries; nevertheless, studies have shown that 20% of suspicious areas are mistakenly diagnosed as dental caries using this technique; hence, diagnosis via radiography alone without an objective assessment is inaccurate. Identifying caries with a deep convolutional neural network-based detector enables the operator to distinguish changes in the location and morphological features of dental caries lesions. Deep learning algorithms have broader and more profound layers and are continually being developed, remarkably enhancing their precision in detecting and segmenting objects.
Conclusion: Clinical applications of deep learning convolutional neural networks in the dental field have shown significant accuracy in detecting and diagnosing dental caries, and these models hold promise in supporting dental practitioners to improve patient outcomes.
Materials and Methods: This study included 101 subjects (46 men, 55 women) from dental patients who received CBCT scans from 2014 to 2020. The patients were divided into those with a low obstructive sleep apnoea (OSA) risk (STOP-Bang score<3) and those with an intermediate to high OSA risk (STOP-Bang score≥3), and their upper airway dimensions were then analysed on CBCT scans. Comparisons between the low-risk and intermediate/high-risk groups were conducted using the t-test and the Mann-Whitney test. Correlations between the total STOP-Bang score and upper airway dimension parameters were established using Spearman correlation coefficients. P values≤0.05 were considered to indicate statistical significance.
Results: Intermediate/high-risk subjects were predominantly male and over 50 years of age, with a higher body mass index. They had significantly longer upper airways, smaller average airway volumes, and smaller widths and antero-posterior dimensions of the narrowest upper airway segment. The total upper airway length was positively correlated with the STOP-Bang score (r s= 0.278). The average volume (r s= -0.203) and width of the narrowest upper airway segment (r s= -0.305) were both negatively correlated with STOP-Bang scores.
Conclusion: Subjects with higher STOP-Bang scores had upper airways that were longer, narrower, and smaller in terms of average volume. CBCT scans taken for dental patients as part of investigative procedures could be correlated with STOP-Bang scores to screen for patients at risk of OSA.
MATERIAL AND METHODS: An online search was performed of the PubMed, Science Direct, and Scopus databases. Based on the selection process, 12 studies were included in this review.
RESULTS: Eleven studies utilized a CNN model for detection tasks, 5 for classification tasks, and 3 for segmentation tasks in the context of tooth numbering on panoramic radiographs. Most of these studies revealed high performance of various CNN models in automating tooth numbering. However, several studies also highlighted limitations of CNNs, such as the presence of false positives and false negatives in identifying decayed teeth, teeth with crown prosthetics, teeth adjacent to edentulous areas, dental implants, root remnants, wisdom teeth, and root canal-treated teeth. These limitations can be overcome by ensuring both the quality and quantity of datasets, as well as optimizing the CNN architecture.
CONCLUSION: CNNs have demonstrated high performance in automated tooth numbering on panoramic radiographs. Future development of CNN-based models for this purpose should also consider different stages of dentition, such as the primary and mixed dentition stages, as well as the presence of various tooth conditions. Ultimately, an optimized CNN architecture can serve as the foundation for an automated tooth numbering system and for further artificial intelligence research on panoramic radiographs for a variety of purposes.
MATERIALS AND METHODS: The study involved the analysis of 28 sets of 3-dimensional (3D) point cloud data, focused on the labial surface of the anterior teeth. These datasets were superimposed within each group in both genuine and imposter pairs. Group A incorporated data from the right to the left central incisor, group B from the right to the left lateral incisor, and group C from the right to the left canine. A comprehensive analysis was conducted, including the evaluation of root mean square error (RMSE) values and the distances resulting from the superimposition of dental arch segments. All analyses were conducted using CloudCompare version 2.12.4 (Telecom ParisTech and R&D, Kyiv, Ukraine).
RESULTS: The distances between genuine pairs in groups A, B, and C displayed an average range of 0.153 to 0.184 mm. In contrast, distances for imposter pairs ranged from 0.338 to 0.522 mm. RMSE values for genuine pairs showed an average range of 0.166 to 0.177, whereas those for imposter pairs ranged from 0.424 to 0.638. A statistically significant difference was observed between the distances of genuine and imposter pairs (P<0.05).
CONCLUSION: The exceptional performance observed for the labial surfaces of anterior teeth underscores their potential as a dependable criterion for accurate 3D dental identification. This was achieved by assessing a minimum of 4 teeth.