Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion.

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early, the treatment will be relatively easy, which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However, the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu’s threshold image enhancement technology, this research solves the problem that the original cutting technology cannot extract certain single teeth, and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN), which can identify caries and restorations from the bitewing images. Moreover, it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image, which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization, (2) a dental image cropping procedure to obtain individually separated tooth samples, and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks, namely, AlexNet, GoogleNet, Vgg19, and ResNet50, experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%, respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film.

Dentists and medical personnel strive to provide patients with prompt medical services. In the past, Dental Panoramic Radiograph (DPR) was often used to diagnose and understand the dental condition of patients. In recent years, many machine learning and deep learning methods have been applied to medical image recognition problems. Moreover, when combined with deep learning methods, data augmentation and image pre-processing methods can also give positive feedback. This study aims to combine data augmentation and data pre-processing methods with advanced deep learning methods to build an innovative and practical two-phase DPR recognition and classification method to assist dentists in diagnosis. It will help to improve the medical quality of dental services by speeding up and saving valuable physician manpower cost and time. Prior to the two-phase recognition based on several effective Convolutional Neural Networks (CNNs), the data augmentation and data pre-processing are processed. In the first phase of this method, the position and numbering of the tooth is automatically classified as one of 32 tooth positions from the DPR tooth images. In the second phase, the dental conditions are automatically recognized from the 6 dental conditions, including orthodontics, endodontic therapy, dental restoration, impaction, implant, and dental prosthesis. The experimental results showed that the trained network, without image pre-processing and augmentation, identified the dental position numbering with an accuracy of 90.93%, and the dental condition with an accuracy of 93.33%. After data augmentation, the accuracy of tooth numbering can be increased to 95.62%, and the accuracy of dental condition can be increased to 98.33%. This is a significant improvement when compared with past research.

The dental panoramic radiograph (DPR) is a pivotal diagnostic tool in dentistry. However, despite the growing prevalence of artificial intelligence (AI) across various medical domains, manual methods remain the prevailing means of interpreting DPR images. This study aims to introduce an advanced identification system for detecting seven dental conditions in DPR images by utilizing Faster R-CNN. The primary objectives are to enhance dentists’ efficiency and evaluate the performance of various CNN models as foundational training networks. This study contributes significantly to the field in several notable ways. Firstly, including a Butterworth filter in the training process yielded an approximately 7% enhancement in judgment accuracy. Secondly, the proposed enhancement technology tailored to different dental symptoms effectively bolstered the training model’s accuracy. Consequently, all dental conditions attained an accuracy rate exceeding 95% in CNN analysis. These accuracy enhancements ranged from 1.34% to 13.24% compared to existing recognition technologies. Thirdly, this study pioneers the application of Faster R-CNN for identifying dental conditions, achieving an impressive accuracy rate of 94.18%. The outcomes of this study mark a substantial advancement compared to prior research and offer dentists a more efficient and convenient means of pre-diagnosing dental conditions.

For daily dental practice, Panoramic (PANO) X-ray film is one of the most commonly used dental X-rays. One of its important advantages is coverage of most anatomic structures and clinical findings in a single film. The important information about clinical treatment and diagnosis can be provided from the expert analysis of the PANO. Combined with the assistance of artificial intelligence, the application has great potential. The purpose of this study was to propose an automated detection system based on several modern convolutional neural networks (CNN) for the classification of retained roots, endodontic treated teeth, and implants. In order to meet the standards of practical clinical application, the database used in this study is provided by dentists with more than three years of practical experience. The contributions of this work are 1) Proposed the more advanced techniques for image segmentation and image position in dental radiographs; 2) A better image enhancement is proposed, which improves the accuracy of the five CNNs to more than 96%; 3) Combined with Fuzzy operation to achieve more powerful and accurate anomaly detection. The final result has an accuracy rate of up to 98.75%. It is about 20% higher than previous techniques. This research designed to identify and document each specific finding automatically could help dentists obtain an objective treatment evaluation, and provide dentists more precious clinical time for dental operations and communication with patients.