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Given the complexity of oral mucosal disease diagnosis and the limitations in the precision of traditional object detection methods, this study aims to develop a high-accuracy artificial intelligence-assisted diagnostic approach based on the SegFormer semantic segmentation model. This method is designed to automatically segment lesion areas in white-light images of oral mucosal diseases, providing objective and quantifiable evidence for clinical diagnosis. This study utilized a dataset of oral mucosal diseases provided by the Affiliated Stomatological Hospital of Zhejiang University School of Medicine, comprising 838 high-resolution images of three diseases: oral lichen planus, oral leukoplakia, and oral submucous fibrosis. These images were annotated at the pixel level by oral specialists using Labelme software (v5.5.0) to construct a semantic segmentation dataset. This study designed a SegFormer model based on the Transformer architecture, employed cross-validation to divide training and testing sets, and compared SegFormer models of different capacities with classical segmentation models such as UNet and DeepLabV3. Quantitative metrics including the Dice coefficient and mIoU were evaluated, and a qualitative visual analysis of the segmentation results was performed to comprehensively assess model performance. The SegFormer-B2 model achieved optimal performance on the test set, with a Dice coefficient of 0.710 and mIoU of 0.786, significantly outperforming other comparative algorithms. The visual results demonstrate that this model could accurately segment the lesion areas of three common oral mucosal diseases. The SegFormer model proposed in this study effectively achieves the precise automatic segmentation of three common oral mucosal diseases, providing a reliable auxiliary tool for clinical diagnosis. It shows promising prospects in improving the efficiency and accuracy of oral mucosal disease diagnosis and has potential clinical application value. [ABSTRACT FROM AUTHOR]

Although guided implant surgery is widely practiced, clinical studies examining the differences in accuracy between implanting systems that use the same surgical guide are currently lacking. This study aimed to evaluate and compare the effects of different dental implanting systems on positioning accuracy using a uniform type of stereolithographic surgical guide to account for cumulative errors in guide production. One hundred BEGO Semados S implants (group A) and 91 NobelActive implants (group B) were inserted into patients using the same type of guide. The accuracy was assessed by matching the preoperative and postoperative cone-beam computerized tomography. The implant shoulder, tip, depth, and angular deviation were registered. Statistically significant differences between groups were determined using Student t test, bivariate correlation test, and generalized estimating equation. The angular deviation was 3.16 ± 1.74° in group A and 2.58 ± 1.41° in group B (P =.013); the depth deviation was 0.44 ± 0.23 mm in group A and 0.51 ± 0.22 mm in group B (P =.032). In terms of vertical accuracy, the Bego implant system is superior to the Nobel implant system using the same type of surgical guide, while the angle accuracy is opposite. Therefore, it is important to control the depth when using the template-guided surgery with the Nobel implant system. Similarly, angle control should be emphasized in the Bego implant system. Measurements of the deviations provide the basis for a clinical reference that will be useful in preoperative analysis for improving the safety and accuracy of guided implant surgical procedures. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Materials and Methods Results Conclusion Trial Registration This study aimed to analyse the relationships between injury variables, treatment variables and the status of the periodontium or pulp of luxated teeth.The electronic medical records and imaging data of patients who underwent treatment for luxation injuries of permanent teeth in the paediatric dentistry and dental emergency departments of the Stomatology Hospital, Zhejiang University School of Medicine between 1 January 2012 and 1 January 2022 were retrospectively analysed. First, the patients' records were reviewed to collect background and follow‐up imaging data; then, all early clinical variables were reviewed and used to construct models to predict the periodontal and pulp status of the luxated teeth. The models included the significant factors identified in the univariate analysis, and multivariate analysis was performed to evaluate the relationships between the variables and the final status of the periodontium or dental pulp. The chi‐square test and Fisher's exact test were employed to analyse the differences in the frequency of each variable. The variables were systematically screened based on their odds ratios, and significance levels were determined by Pearson's chi‐square test.A total of 188 patients were initially identified and ultimately included. The age range of the patients was 7–56 years. Logistic regression models for periodontal and pulp prognosis of the luxated teeth were established. The models were refined by combining the results of feature selection, parameter testing and goodness‐of‐fit testing. The final model included four variables and accurately predicted the periodontal outcome in 65.79% of the cases. The prognostic model for the dental pulp included three significant factors and had an overall accuracy of 94.59%.The prognostic models developed to predict the influence of various factors on the status of the periodontium and dental pulp of luxated teeth demonstrated notable accuracy and practical utility. Therefore, these models are potentially valuable tools for long‐term prognostic assessments.Approval no. ChiCTR2100044897 [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to provide a universal and reliable reference system quantifying temporomandibular joint (TMJ) morphological and positional changes., Methods: Large field-of-view (FOV) cone-beam computed tomography (CBCT) images (20 TMJs) from 10 preorthognathic surgery patients and limited FOV CBCT images (40 TMJs) from 20 splint therapy-treated patients with temporomandibular disorders were collected. TMJ-specific reference system including a TMJ horizontal reference plane (TMJHP) and a local coordinate system (TMJCS) was constructed with landmarks on cranial base. Its application for TMJ measurements and its spatial relationship to common Frankfort horizontal plane (FHP) and maxillofacial coordinate system (MFCS) were evaluated., Results: Five relevant landmarks were selected to optimally construct TMJ-specific reference system. General parallelism between TMJHP and FHP was demonstrated by minimal angular and constant distance deviation (1.714 ± 0.811º; 2.925 ± 0.817 mm). Additionally, tiny axial orientational deviations (0.181 ± 6.805º) suggested TMJCS rivaled MFCS. Moreover, small deviations in orientations and distances (1.232 ± 0.609º; 0.310 ± 0.202 mm) indicated considerable reliability for TMJCS construction, with intraclass correlation coefficients (ICCs) ranging from 0.999 to 1.000. Lastly, slight discrepancies in translations and rotations revealed high reliability for condylar positional and morphological measurements (ICC, 0.918-0.999)., Limitations: TMJ-specific reference system was merely tested in two representative FOVs., Conclusions: This study provides a universal and reliable reference system for TMJ assessment that is applicable to both limited and large FOV CBCT. It would improve comparability among diverse studies and enable comprehensive evaluations of TMJ positional and morphological changes during TMJ-related treatment follow-up such as splint therapy and disease progression., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Orthodontic Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

For patients with partial jaw defects, cysts and dental implants, doctors need to take panoramic X-ray films or manually draw dental arch lines to generate Panorama images in order to observe their complete dentition information during oral diagnosis. In order to solve the problems of additional burden for patients to take panoramic X-ray films and time-consuming issue for doctors to manually segment dental arch lines, this paper proposes an automatic panorama reconstruction method based on cone beam computerized tomography (CBCT). The V-network (VNet) is used to pre-segment the teeth and the background to generate the corresponding binary image, and then the Bezier curve is used to define the best dental arch curve to generate the oral panorama. In addition, this research also addressed the issues of mistakenly recognizing the teeth and jaws as dental arches, incomplete coverage of the dental arch area by the generated dental arch lines, and low robustness, providing intelligent methods for dental diagnosis and improve the work efficiency of doctors.