Training on the haptic VR simulator and conventional phantom head had equivalent effects on minimizing procedural errors in endodontic access cavity preparation.

ObjectivesAmeloblastomas and keratocystic odontogenic tumors (KCOTs) are important odontogenic tumors of the jaw. While their radiological findings are similar, the behaviors of these two types of tumors are different. Precise preoperative diagnosis of these tumors can help oral and maxillofacial surgeons plan appropriate treatment. In this study, we created a convolutional neural network (CNN) for the detection of ameloblastomas and KCOTs.MethodsFive hundred digital panoramic images of ameloblastomas and KCOTs were retrospectively collected from a hospital information system, whose patient information could not be identified, and preprocessed by inverse logarithm and histogram equalization. To overcome the imbalance of data entry, we focused our study on 2 tumors with equal distributions of input data. We implemented a transfer learning strategy to overcome the problem of limited patient data. Transfer learning used a 16-layer CNN (VGG-16) of the large sample dataset and was refined with our secondary training dataset comprising 400 images. A separate test dataset comprising 100 images was evaluated to compare the performance of CNN with diagnosis results produced by oral and maxillofacial specialists.ResultsThe sensitivity, specificity, accuracy, and diagnostic time were 81.8%, 83.3%, 83.0%, and 38 seconds, respectively, for the CNN. These values for the oral and maxillofacial specialist were 81.1%, 83.2%, 82.9%, and 23.1 minutes, respectively.ConclusionsAmeloblastomas and KCOTs could be detected based on digital panoramic radiographic images using CNN with accuracy comparable to that of manual diagnosis by oral maxillofacial specialists. These results demonstrate that CNN may aid in screening for ameloblastomas and KCOTs in a substantially shorter time.

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Background Oral cancer is a deadly disease among the most common malignant tumors worldwide, and it has become an increasingly important public health problem in developing and low‐to‐middle income countries. This study aims to use the convolutional neural network (CNN) deep learning algorithms to develop an automated classification and detection model for oral cancer screening. Methods The study included 700 clinical oral photographs, collected retrospectively from the oral and maxillofacial center, which were divided into 350 images of oral squamous cell carcinoma and 350 images of normal oral mucosa. The classification and detection models were created by using DenseNet121 and faster R‐CNN, respectively. Four hundred and ninety images were randomly selected as training data. In addition, 70 and 140 images were assigned as validating and testing data, respectively. Results The classification accuracy of DenseNet121 model achieved a precision of 99%, a recall of 100%, an F1 score of 99%, a sensitivity of 98.75%, a specificity of 100%, and an area under the receiver operating characteristic curve of 99%. The detection accuracy of a faster R‐CNN model achieved a precision of 76.67%, a recall of 82.14%, an F1 score of 79.31%, and an area under the precision‐recall curve of 0.79. Conclusion The DenseNet121 and faster R‐CNN algorithm were proved to offer the acceptable potential for classification and detection of cancerous lesions in oral photographic images.