OBJECTIVES: This study aims to predict the risk of deep caries exposure in radiographic images based on the convolutional neural network model, compare the prediction results of the network model with those of senior dentists, evaluate the performance of the model for teaching and training stomatological students and young dentists, and assist dentists to clarify treatment plans and conduct good doctor-patient communication before surgery. METHODS: A total of 206 cases of pulpitis caused by deep caries were selected from the Department of Stomatological Hospital of Tianjin Medical University from 2019 to 2022. According to the inclusion and exclusion criteria, 104 cases of pulpitis were exposed during the decaying preparation period and 102 cases of pulpitis were not exposed. The 206 radiographic images collected were randomly divided into three groups according to the proportion: 126 radiographic images in the training set, 40 radiographic images in the validation set, and 40 radiographic images in the test set. Three convolutional neural networks, visual geometry group network (VGG), residual network (ResNet), and dense convolutional network (DenseNet) were selected to analyze the rules of the radiographic images in the training set. The radiographic images of the validation set were used to adjust the super parameters of the network. Finally, 40 radiographic images of the test set were used to evaluate the performance of the three network models. A senior dentist specializing in dental pulp was selected to predict whether the deep caries of 40 radiographic images in the test set were exposed. The gold standard is whether the pulp is exposed after decaying the prepared hole during the clinical operation. The prediction effect of the three network models (VGG, ResNet, and DenseNet) and the senior dentist on the pulp exposure of 40 radiographic images in the test set were compared using receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score to select the best network model. RESULTS: The best network model was DenseNet model, with AUC of 0.97. The AUC values of the ResNet model, VGG model, and the senior dentist were 0.89, 0.78, and 0.87, respectively. Accuracy was not statistically different between the senior dentist (0.850) and the DenseNet model (0.850)(P>0.05). Kappa consistency test showed moderate reliability (Kappa=0.6>0.4, P<0.05). CONCLUSIONS Among the three convolutional neural network models, the DenseNet model has the best predictive effect on whether deep caries are exposed in imaging. The predictive effect of this model is equivalent to the level of senior dentists specializing in dental pulp.
Humans ; Deep Learning ; Neural Networks, Computer ; Pulpitis/diagnostic imaging* ; Reproducibility of Results ; ROC Curve ; Random Allocation

OBJECTIVECurved canal preparation is much difficult in root canal therapy(RCT). Step back technique and routine technique are still regular methods in curved canal preparation. The purpose of this study was to introduce a new method reverse flaring technique, and to investigate its preparation efficiency in intermediate-curvature canals.

METHODS48 cases of lower first molars RCT were collected, which were first treated because of pulpitis or apical periodontitis in West China College of Stomatology, Sichuan University from Nov. 2001 to Aug. 2003, mesial canal curvature was intermediate (30 degrees-60 degrees), determined by Schineider method. Cases were divided into two groups, in reverse flaring technique group, canal preparation in 27 cases were finished by reverse flaring technique, 21 cases by step back technique as control. In working length determination and fitting master cone stages, cases in two groups which fit full working length were recorded, determined by radiograph, and analyzed by chi 2 test.

RESULTSIn working length determination stage, cases which fit full working length in reverse flaring technique group were significantly more than that of step back technique group (P < 0.05), in fitting master cone stage, cases which fit full working length in reverse flaring technique group were also significantly more than that of step back technique group(P < 0.05).

CONCLUSIONIn working length determination stage, cases which fit full working length in reverse flaring technique group were significantly more than that of step back technique group (P < 0.05), in fitting master cone stage, cases which fit full working length in reverse flaring technique group were also significantly more than that of step back technique group(P < 0.05).

Adult ; Dental Pulp Cavity ; anatomy & histology ; diagnostic imaging ; Female ; Humans ; Male ; Middle Aged ; Molar ; Periodontitis ; diagnostic imaging ; therapy ; Pulpitis ; diagnostic imaging ; therapy ; Radiography ; Root Canal Preparation ; instrumentation ; methods ; Root Canal Therapy ; Tooth Apex ; anatomy & histology ; diagnostic imaging

OBJECTIVETo evaluate the quality of root canal filling performed by dentists and advanced dental trainees (ADTs) and the current level of continuing education in a dental teaching hospital.

METHODS2 043 cases, randomly completed by six dentists and eight ADTs over half a year, were divided into two groups. The quality of root canal filling was analyzed radiographically.

RESULTSThe overall percentage of the adequate root filling was 49.6%. The percentages of the adequate teeth filling and root canals filling (59.9%; 63.9%) by dentists were significantly higher than those (40.1%; 47.4%) by ADTs. In addition, the frequency of the adequate root canals filling by ADTs in the last two months (57.8%) was significantly higher than that in the first two months (40.0%), and the adequate filling rate by ADTs during the last two months was close to that by dentists.

CONCLUSIONSThe quality of root canal filling performed by dentists was adequate. There was a substantial improvement for ADTs in filling quality after six months training.

Dental Pulp Cavity ; diagnostic imaging ; Education, Dental, Continuing ; Hospitals, Teaching ; Humans ; Periodontitis ; therapy ; Pulpitis ; therapy ; Quality of Health Care ; Radiography ; Root Canal Obturation ; standards

This case report describes the diagnosis and endodontic therapy of maxillary fused second and third molars, using cone-beam computed tomography (CBCT). A 31-year-old Chinese male, with no contributory medical or family/social history, presented with throbbing pain in the maxillary right molar area following an unsuccessful attempted tooth extraction. Clinical examination revealed what appeared initially to be a damaged large extra cusp on the buccal aspect of the distobuccal cusp of the second molar. However, CBCT revealed that a third molar was fused to the second molar. Unexpectedly, the maxillary left third molar also was fused to the second molar, and the crown of an unerupted supernumerary fourth molar was possibly also fused to the apical root region of the second molar. Operative procedures should not be attempted without adequate radiographic investigation. CBCT allowed the precise location of the root canals of the right maxillary fused molar teeth to permit successful endodontic therapy, confirmed after 6 months.
Adult ; Cone-Beam Computed Tomography ; methods ; Follow-Up Studies ; Fused Teeth ; diagnostic imaging ; Humans ; Image Processing, Computer-Assisted ; methods ; Imaging, Three-Dimensional ; methods ; Male ; Maxilla ; Molar ; abnormalities ; Molar, Third ; abnormalities ; Pulpitis ; diagnostic imaging ; Root Canal Therapy ; Tooth Root ; abnormalities ; Tooth, Supernumerary ; diagnostic imaging ; Tooth, Unerupted ; diagnostic imaging