Objectives: Deep learning methods have achieved impressive diagnostic performance in the field of radiology. The current study aimed to use deep learning methods to detect caries lesions, classify different radiographic extensions on panoramic films, and compare the classification results with those of expert dentists. Methods: A total of 1160 dental panoramic films were evaluated by three expert dentists. All caries lesions in the films were marked with circles, whose combination was defined as the reference dataset. A training and validation dataset (1071) and a test dataset (89) were then established from the reference dataset. A convolutional neural network, called nnU-Net, was applied to detect caries lesions, and DenseNet121 was applied to classify the lesions according to their depths (dentin lesions in the outer, middle, or inner third D1/2/3 of dentin). The performance of the test dataset in the trained nnU-Net and DenseNet121 models was compared with the results of six expert dentists in terms of the intersection over union (IoU), Dice coefficient, accuracy, precision, recall, negative predictive value (NPV), and F1-score metrics. Results: nnU-Net yielded caries lesion segmentation IoU and Dice coefficient values of 0.785 and 0.663, respectively, and the accuracy and recall rate of nnU-Net were 0.986 and 0.821, respectively. The results of the expert dentists and the neural network were shown to be no different in terms of accuracy, precision, recall, NPV, and F1-score. For caries depth classification, DenseNet121 showed an overall accuracy of 0.957 for D1 lesions, 0.832 for D2 lesions, and 0.863 for D3 lesions. The recall results of the D1/D2/D3 lesions were 0.765, 0.652, and 0.918, respectively. All metric values, including accuracy, precision, recall, NPV, and F1-score values, were proven to be no different from those of the experienced dentists. Conclusion: In detecting and classifying caries lesions on dental panoramic radiographs, the performance of deep learning methods was similar to that of expert dentists. The impact of applying these well-trained neural networks for disease diagnosis and treatment decision making should be explored.

This study aimed to develop a novel detection model for automatically assessing the real contact relationship between mandibular third molars (MM3s) and the inferior alveolar nerve (IAN) based on panoramic radiographs processed with deep learning networks, minimizing pseudo-contact interference and reducing the frequency of cone beam computed tomography (CBCT) use. A deep-learning network approach based on YOLOv4, named as MM3-IANnet, was applied to oral panoramic radiographs for the first time. The relationship between MM3s and the IAN in CBCT was considered the real contact relationship. Accuracy metrics were calculated to evaluate and compare the performance of the MM3–IANnet, dentists and a cooperative approach with dentists and the MM3–IANnet. Our results showed that in comparison with detection by dentists (AP = 76.45%) or the MM3–IANnet (AP = 83.02%), the cooperative dentist–MM3–IANnet approach yielded the highest average precision (AP = 88.06%). In conclusion, the MM3-IANnet detection model is an encouraging artificial intelligence approach that might assist dentists in detecting the real contact relationship between MM3s and IANs based on panoramic radiographs.

Background: When a tooth is diagnosed with irreversible pulpitis, root canal therapy (RCT) is generally performed to completely remove pulp tissue, which might lead to a higher risk of loss of vascularity, and teeth being more prone to fracture. Vital pulp therapy (VPT) is a personalized method of treating irreversible pulpitis, which conforms to the trend of minimally invasive endodontics. The remaining vital pulp could promote the physiological development of the roots of young permanent teeth with incomplete apical foramen. However, clear guidelines for VPT indication are still missing. Objective: This prospective cohort study evaluated the outcomes of vital pulp therapy (VPT) using iRoot BP Plus (Innovative Bioceramix Inc, Vancouver, BC, Canada) in permanent teeth of 6- to 20-year-old patients with irreversible pulpitis caused by caries and analyzed the preoperative factors affecting VPT prognosis. Methods: Fifty-nine permanent teeth in 59 patients with irreversible pulpitis caused by caries were treated with VPT using iRoot BP Plus. All patients received VPT under a standardized protocol. After informed consent, teeth were isolated with a dental dam, then operators performed VPT with iRoot BP Plus and restored the teeth with composite resin or stainless steel crown. Patients were postoperatively recalled after 3, 6 and 12 months and then recalled annually. Successful cases were defined as successful in both clinical and radiographic evaluations. A statistical analysis was performed using the Fisher exact test, and the level of significant difference was p < 0.05. Results: After 6–36 months of follow-up, a total of 57 teeth from 57 patients were accessible for evaluation. The mean age of subjects was 11.75 ± 3.81 years. The overall clinical and radiographic success rate of VPT was 91.2% (52/57). With an observation time of one year or more, the success rate was 90.5% (38/42). All the symptoms and physical examination findings showed no significant effect on VPT prognosis (p > 0.05) using a binary logistic regression model. Conclusions: Permanent teeth in 6- to 20-year-old patients diagnosed as irreversible pulpitis caused by caries can be successfully treated with VPT using iRoot BP Plus.