Journal of Dental Sciences最新文献

Background/purpose: Oral mucosal lesions are associated with a variety of pathological conditions. Most deep-learning-based convolutional neural network (CNN) systems for computer-aided diagnosis of oral lesions have typically concentrated on determining limited aspects of differential diagnosis. This study aimed to develop a CNN-based diagnostic model capable of classifying clinical photographs of oral ulcerative and associated lesions into five different diagnoses, thereby assisting clinicians in making accurate differential diagnoses.

Materials and methods: A set of clinical images were selected, including 506 images of five different diagnoses. The images were pre-processed and randomly divided into two sets for training and testing the CNN model. The model architecture was composed of convolutional layers, batch normalization layers, max pooling layers, the dropout layer and fully-connected layers. Evaluation metrics included weighted-precision, weighted-recall, weighted-F1 score, average specificity, Cohen's Kappa coefficient, normalized confusion matrix and AUC.

Results: The overall performance for the image classification showed a weighted-precision of 88.8%, a weighted-recall of 88.2%, a weighted-F1 score of 0.878, an average pecificity of 97.0%, a Kappa coefficient of 0.851, and an average AUC of 0.985.

Conclusion: The model achieved a decent classification performance (overall AUC=0.985), showing the capacity to discern between benign and malignant potential lesions, and laid the foundation of a novel tool that can help clinical differential diagnosis of oral mucosal lesions. The main challenges were the small and imbalanced dataset. Enlarging the minority classes, incorporating more oral mucosal lesion diagnoses, employing transfer learning and cross-validation might be included in future works to optimize the image classification model.

Background/purpose: Traditional dental education faces challenges, such as high student-to-faculty ratios and disruptions like the COVID-19 pandemic, which limit hands-on learning opportunities. Digital technologies, including intraoral scanners, offer potential solutions by improving accuracy and efficiency in clinical practice. This study explored the integration of digital tools in a self-directed learning model for the fixed prosthodontic tooth preparation.

Materials and methods: This study, conducted with 81 fourth-year dental students, incorporated digital tools like intraoral scanners into practical training. Students completed a pre-intervention evaluation, followed by training on digital analysis tools, and concluded with a self-directed learning protocol. The study assessed students' theoretical knowledge and practical skills using pre- and post-intervention tests, digital scans, and feedback questionnaires. Statistical analyses, including paired t-tests, evaluated the effectiveness of the intervention.

Results: Significant improvements were observed in both theoretical knowledge (pre-test 86 %, post-test 98 %, P = 0.012) and practical skills, with the percentage of "perfect" crown preparations rising from 14 % to 73 % (P < 0.0001). Occlusal reduction showed improvement but remained challenging for some students. Digital tools reduced student anxiety, with 77 % of students reporting decreased anxiety during practical exercises.

Conclusion: Integrating digital scanning technology with traditional teaching enhanced student competence in tooth preparation, reduced anxiety, and provided objective evaluation criteria. The self-directed learning model supported skill development and independent problem-solving, indicating the potential for broader application in dental education. Future research should explore long-term impacts on clinical performance and optimize digital tool integration throughout the curriculum.

Background: /purpose: The metabolic by-product butyric acid of Gram-negative anaerobic bacteria can invoke pathological effects on periodontal cells resulting in inflammation and further destruction of periodontium. However, limited researches on the effects of butyric acid on cementoblasts were reported. Therefore, this study aimed to investigate the type of cell death in murine cementoblast (OCCM.30) caused by adding the different concentrations of sodium butyrate to the cell culture.

Materials and methods: OCCM.30 cells were exposed to sodium butyrate (0, 2, 4, 8, 16 mM) for 48 h. Cell viability was determined by microculture tetrazolium assay. Cell cycle distribution and cell death were analyzed by flow cytometry. Caspase-mediated apoptotic cascade was evaluated by Western blot.

Results: The concentrations of sodium butyrate≧4 mM were found to inhibit cell viability of OCCM.30 cells in a dose-dependent manner (P < 0.05). Sodium butyrate elevated sub-G1 cell population which exhibited cell apoptosis in OCCM.30 cells (P < 0.05). In addition, early and later apoptotic cells were found in sodium butyrate-induced cell death. Sodium butyrate significantly stimulated the degradation of procaspases-3, -8, and -9 levels, respectively (P < 0.05). Simultaneously, sodium butyrate corresponded to augment the levels of cleaved forms of caspases-3, -8, and -9, respectively (P < 0.05).

Conclusion: Taken together, sodium butyrate is a cytotoxic agent and can induce apoptosis on cementoblasts. The pathway involved in apoptosis is activated by caspase family signaling pathways. These evidences may provide a new mechanistic insight into the mechanism of damage of cementoblasts during the development and progression of periodontitis.

Background/purpose: Identifying crestal bone level (CBL) on the buccal and lingual aspects poses challenges in conventional dental radiographs. Given that optical coherence tomography (OCT) has the capability to non-invasively provide in-depth information about the periodontium, this in vitro study aimed to assess whether OCT can effectively identify periodontal landmarks and measure CBL in the presence of gingiva.

Materials and methods: An in-house handheld scanning probe connected to a 1310-nm swept-source OCT (SS-OCT) system, along with self-developed algorithms were employed to measure the CBL in dental models with artificial gingiva. Markers were positioned 0.5 mm above the artificial gingival margin (SG) and the crestal bone (SC) on both the mid-buccal and mid-lingual sides of 28 plastic teeth. The distances between the paired SG and SC were measured in the OCT images after correcting for the optical path through the covering artificial gingiva. These measurements were subsequently compared to the ground truth values obtained using a 2.5D inspection system.

Results: The mean difference in CBL measured by SS-OCT and 2.5D was 0.008 mm (95 % CI: -0.092 to 0.108 mm). Statistical analysis using a three-way ANOVA indicated that the measurement differences were not significant across maxillary/mandible, anterior/posterior, and buccal/lingual dimensions. Furthermore, these differences were not associated with gingival thickness (α = 0.05).

Conclusion: The proposed SS-OCT system demonstrated its capability to accurately and non-invasively assess CBL through artificial gingiva. Moreover, it facilitated the semi-automatic delineation of critical periodontal landmarks on OCT en face images, highlighting its potential for clinical applications.

Background/purpose: Temporomandibular joint (TMJ) arthritis causes inflammation and degradation of the mandibular condylar cartilage and subchondral bone. Complete Freund's adjuvant (CFA) and collagen-induced arthritis (CIA) are models for studying TMJ arthritis. While micro-computed tomography (micro-CT) is crucial for three-dimensional (3D) bone analysis, it has limitations in imaging nonmineralized tissues. Phosphotungstic acid (PTA) enhances soft tissue contrast. However, research on the 3D imaging of mandibular condylar cartilage and the molecular mechanisms of CFA- and CIA-induced arthritis remains unclear. This study aimed to investigate the bone and PTA-stained cartilage in the mandibular condyle using 3D reconstruction and explore the characteristics of enriched gene ontology terms underlying CFA- and CIA-induced TMJ arthritis in rat models.

Materials and methods: Rat mandibular condyles were collected from control, CFA, and CIA groups. Live micro-CT created 3D bone structures, and PTA-enhanced micro-CT constructed 3D mandibular condylar cartilage. Gene ontology enrichment analysis identified enriched gene ontology terms from differentially expressed genes through RNA sequencing.

Results: Major deformities in cartilage volume and bone morphology were observed in the arthritis-induced groups. The CIA group exhibited significant correlations between cartilage volume and bone parameters changes. Gene ontology enrichment analysis indicated fewer terms with upregulated differentially expressed genes related to inflammation and immune response in the CIA group than in the CFA group.

Conclusion: This study reveals distinct responses between CFA- and CIA-induced TMJ arthritis models. The CIA group exhibited strong correlations between cartilage volume and bone parameter changes and had less pronounced inflammation and immune response than the CFA group.

Background: Proliferative verrucous leukoplakia (PVL) is a special type of leukoplakia characterized by high rate of malignant transformation into oral squamous cell carcinoma (OSCC). This study aimed to analyze the canceration risk and prognostic factors of PVL and establish effective diagnostic and prognostic predictive models.

Materials and methods: A total of 467 patients were enrolled, including 170 cases of PVL. The independent risk and prognostic factors of PVL were analyzed by univariable and multivariable logistic regression. Nomogram models were constructed to predict the canceration risk and prognosis of PVL. The predictive power was evaluated by Hosmer-Lemeshow test, receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis.

Results: Multivariable logistic regression analyses identified that canceration risk factors of PVL included sex, lesion sites, clinical presentation, non-smoker and oral epithelial dysplasia (OED). The independent prognostic factors of PVL were sex, clinical presentation, local irritants and OED. Diagnosis and prognostic nomogram models were constructed. The areas under the ROC curve were 0.945 and 0.893, respectively. The calibration plots showed strong agreement between the prediction and observation. Decision curve analysis indicated that the models provided significant clinical benefits for patients.

Conclusion: Our study established and validated the diagnosis and prognostic predictive nomogram models, which were accurate to predict the canceration risk and prognostic factors of PVL, providing individualized clinical decisions for clinical work.