Dental Images Research Articles

A novel method of human identification based on dental impression image

In large-scale natural disasters and special criminal cases, surface features of bodies, such as faces and fingerprints, are easily destroyed. Teeth possess strong high-temperature resistance, corrosion resistance, and high hardness, which can compensate for the shortcomings of the aforementioned situations. This paper proposes an identification method based on the aggregated features of multi-scale dental impression images. Firstly, a method exploiting the adaptive object detection method based on YOLOv8 is proposed to segment toothprints. Next, a novel geometric feature named calibrated offset distance is extracted, combined with the SIFT feature method, to extract multi-scale and multi-dimensional features from the global toothprint, local toothprints, and single-tooth prints. Finally, all features are aggregated to enhance the descriptive ability and robustness. Experimental results indicate that the method proposed in this paper demonstrates good identification performance.

Dental imaging for dental nurses

Sharon Grant highlights the requirements and knowledge needed in order to work within radiography. Aim To provide advice regarding training requirements for dental nurses to become radiography and CBCT operators. Objectives Understand the reasons behind the training requirements to become a radiography operator. Appreciate the different methods of imaging available.

Deep Learning-Based Recognition of Periodontitis and Dental Caries in Dental X-ray Images.

Dental X-ray images are important and useful for dentists to diagnose dental diseases. Utilizing deep learning in dental X-ray images can help dentists quickly and accurately identify common dental diseases such as periodontitis and dental caries. This paper applies image processing and deep learning technologies to dental X-ray images to propose a simultaneous recognition method for periodontitis and dental caries. The single-tooth X-ray image is detected by the YOLOv7 object detection technique and cropped from the periapical X-ray image. Then, it is processed through contrast-limited adaptive histogram equalization to enhance the local contrast, and bilateral filtering to eliminate noise while preserving the edge. The deep learning architecture for classification comprises a pre-trained EfficientNet-B0 and fully connected layers that output two labels by the sigmoid activation function for the classification task. The average precision of tooth detection using YOLOv7 is 97.1%. For the recognition of periodontitis, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve is 98.67%, and the AUC of the precision-recall (PR) curve is 98.38%. For the recognition of dental caries, the AUC of the ROC curve is 98.31%, and the AUC of the PR curve is 97.55%. Different from the conventional deep learning-based methods for a single disease such as periodontitis or dental caries, the proposed approach can provide the recognition of both periodontitis and dental caries simultaneously. This recognition method presents good performance in the identification of periodontitis and dental caries, thus facilitating dental diagnosis.

A review of deep learning in dentistry

Oral diseases have a significant impact on human health, often going unnoticed in their early stages. Deep learning, a promising field in artificial intelligence, has shown remarkable success in various domains, especially dentistry. This paper aims to provide an overview of recent research on deep learning applications in dentistry, with a focus on dental imaging. Deep learning algorithms perform well in difficult tasks such as image segmentation and recognition, enabling accurate identification of oral conditions and abnormalities. Integration of deep learning with other oral health data offers a holistic understanding of the relationship between oral and systemic health. However, there are still many challenges that need to be addressed.

Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review.

Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to the clinical importance of deep learning. In addition, the review critically evaluates the literature to guide and further develop future studies in this field. An extensive literature search was conducted for the 2019-May 2023 range using the Medline (PubMed) and Google Scholar databases to identify eligible articles, and 101 studies were shortlisted, including applications for diagnosing dental anomalies (n = 22) and diseases (n = 79) using deep learning for classification, object detection, and segmentation tasks. According to the results, the most commonly used task type was classification (n = 51), the most commonly used dental image material was panoramic radiographs (n = 55), and the most frequently used performance metric was sensitivity/recall/true positive rate (n = 87) and accuracy (n = 69). Dataset sizes ranged from 60 to 12,179 images. Although deep learning algorithms are used as individual or at least individualized architectures, standardized architectures such as pre-trained CNNs, Faster R-CNN, YOLO, and U-Net have been used in most studies. Few studies have used the explainable AI method (n = 22) and applied tests comparing human and artificial intelligence (n = 21). Deep learning is promising for better diagnosis and treatment planning in dentistry based on the high-performance results reported by the studies. For all that, their safety should be demonstrated using a more reproducible and comparable methodology, including tests with information about their clinical applicability, by defining a standard set of tests and performance metrics.

Mask-Transformer-Based Networks for Teeth Segmentation in Panoramic Radiographs.

Teeth segmentation plays a pivotal role in dentistry by facilitating accurate diagnoses and aiding the development of effective treatment plans. While traditional methods have primarily focused on teeth segmentation, they often fail to consider the broader oral tissue context. This paper proposes a panoptic-segmentation-based method that combines the results of instance segmentation with semantic segmentation of the background. Particularly, we introduce a novel architecture for instance teeth segmentation that leverages a dual-path transformer-based network, integrated with a panoptic quality (PQ) loss function. The model directly predicts masks and their corresponding classes, with the PQ loss function streamlining the training process. Our proposed architecture features a dual-path transformer block that facilitates bi-directional communication between the pixel path CNN and the memory path. It also contains a stacked decoder block that aggregates multi-scale features across different decoding resolutions. The transformer block integrates pixel-to-memory feedback attention, pixel-to-pixel self-attention, and memory-to-pixel and memory-to-memory self-attention mechanisms. The output heads process features to predict mask classes, while the final mask is obtained by multiplying memory path and pixel path features. When applied to the UFBA-UESC Dental Image dataset, our model exhibits a substantial improvement in segmentation performance, surpassing existing state-of-the-art techniques in terms of performance and robustness. Our research signifies an essential step forward in teeth segmentation and contributes to a deeper understanding of oral structures.

Human Dental Age and Gender Assessment from Dental Radiographs Using Deep Convolutional Neural Network

Human gender and age identification play a prominent role in forensics, bio-archaeology, and anthropology. Dental images provide prominent indications used for the treatment or diagnosis of disease and forensic investigation. Numerous dental age identification techniques come with specific boundaries, namely minimum reliability and accuracy. Gender identification approaches are not widely researched, whereas the effectiveness and accuracy of classification are not practical and very minimal. Drawbacks in the existing system are considered in the formulation of the proposed approach. Deep learning approaches can effectively rectify issues of drawbacks in other classifiers. The accuracy and performance of a classifier are enhanced with the deep convolutional neural network. The fuzzy C-Means Clustering approach is used for segmentation, and Ant Lion Optimization is used for optimal feature score selection. The selected features are classified using a deep convolutional neural network (DCNN). The performance of the proposed technique is investigated with existing classifiers, and DCNN outperforms other classifiers. The proposed technique achieves 91.7% and 91% accuracy for the identification of gender and age, respectively.

Application of Panoramic Dental X-Ray Images Denoising

Dental X-ray imaging helps dentists detect many problems such as caries, cysts and jaw structure problems. Clinical diagnosis and preventive examinations of dental structures play an important role by providing a comprehensive imaging evaluation with panoramic x-rays for dentists. However, researchers primarily use image processing methods to analyze and improve a dental X-ray image and increase its contribution to the diagnostic time. Image segmentation, classification, threshold-based analysis, artificial neural networks, frequency-based methods are the most widely used image processing techniques to analyze medical images and assist in the development of computer aided medical diagnosis systems. In this study, images were analyzed in terms of noise removal by using convolutional neural networks and binary and wavelet filters to improve the images that were distorted and lost their clarity as a result of noise caused by various reasons during shooting. The performances of these methods were compared and it was seen that successful results were obtained in different noise types by using convolutional neural networks.

Investigation of Extraoral Dental Radiography Examination Rejections at a Dental and Oral Hospital in Semarang, Indonesia

Background : Extraoral radiography techniques, such as panoramic radiography and cephalometric radiography, are used to obtain an overall view of the teeth, jaws, and surrounding tissues. However, there are instances where radiographic images are rejected or marked as inadequate based on the required quality standards. Reject analysis is a method of evaluation that involves analyzing rejected dental radiographic images. This research will identify the most common factors contributing to image rejection and provide recommendations for improving the quality of dental radiographic images Methode : The type of scientific study in this research is quantitative and descriptive. The research was conducted at the Radiodiagnostic Department of the Dental Hospital, Muhammadiyah University of Semarang. The total sample size taken was 301 extraoral radiographic films. The research data were obtained through a retrospective survey approach and observation method. The analysis was performed using Microsoft Excel 2021 application. Result : The primary factors contributing to film rejection are patient movement (50%), patient positioning (25%), and device settings (25%). Film rejection for extraoral radiographic examinations consisted of 2 films in Panoramic examinations, accounting for 50%, and 2 films in Cephalometric examinations, also accounting for 50% Conclusion : The film rejection ratio for extraoral dental radiography examinations conducted at the Radiodiagnostic Department of the Dental Hospital, Universitas Muhammadiyah Semarang, remained below the normal limit value of 1.29%.

A Case of Rhinitis Caused by Severe Periodontitis in a Maltese

A 2.8kg, 10-year-old, spayed female, maltese presented to the hospital with sudden sneezing and nasal discharge symptoms. On physical examination, the discharge of secretions came out through the bi-nostrils and abundant calculus, generalized gingivitis, and movement of several teeth in dental examinations. Dental radiographs showed periapical lucencies of the right maxillary canine and left fourth premolar. After scaling and multiple extractions including right maxillary canine and left fourth premolar, the clinical symptoms totally disappeared. Then, clindamycin 11mg/kg, orally, q12h was prescribed for 14 days. After treatment, the patient's nasal disease improved. Among the causes of rhinitis in dogs, periodontitis is a common cause. If a patient with a poor dental condition shows nasal discharge accompanied by sneezing, it is necessary to consider checking for periodontitis through dental X-ray imaging or CT scan.

A Literature Review on Dental Disease Diagnosis

Artificial intelligence (AI) has emerged as a transformative technology in various fields, including dentistry. This literature review provides an overview of recent research papers that explore the application of AI in dentistry and its impact on clinical practice. The selected papers cover a wide range of topics, including dental disease detection, dental imaging analysis, dental education, and diagnostic accuracy assessment. In the domain of dental disease detection, the use of AI algorithms has shown promising results in automating the detection of dental pathologies such as caries, periodontal diseases, and lesions. These algorithms have the potential to improve accuracy, enable early intervention, and enhance treatment planning. Furthermore, the integration of AI in dental imaging analysis has enabled advanced image interpretation and segmentation. Studies have demonstrated the effectiveness of AI algorithms in analyzing panoramic radiographs, cone-beam computed tomography (CBCT), and intraoral radiography, leading to improved identification of dental conditions such as root fractures and periapical radiolucent lesions. Moreover, the integration of AI in dental education has revolutionized the learning experience. Virtual reality simulations, computer-assisted learning, and AI-based assessment tools have enhanced student engagement, skill acquisition, and curriculum updates.Overall, this literature review highlights the potential of AI in transforming dentistry. The selected papers contribute to the growing body of knowledge on AI applications in dentistry and pave the way for further research and advancements in this field. The integration of AI has the potential to revolutionize dental practice, improve diagnostic accuracy, optimize treatment planning, and elevate the quality of patient care.

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health.

Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues.

The Use of a 3D Image Comparison Program for Dental Identification

Dental identification involves compiling a prescribed dental chart of a deceased person’s oral findings which is then compared with antemortem dental information. However, this process is complicated, and a comparison can be difficult. In this study, the authors evaluated whether it is possible to identify images from antemortem dental information images using an image comparison program (AKAZE) with one-sided cross-sectional images generated from the STL (Standard Triangle Language) data of upper and lower jaw models acquired with an intraoral scanner. From the STL data of 20 patients, 120 cross-sectional images were generated by three practitioners and compared with the cross-sectional images of 20 patients generated later, and the degree of agreement calculated by AKAZE was analyzed. Statistically significant differences were found between images of the same and different models, and statistically significant differences were obtained when comparing one-sided images with limited information, suggesting that partial dentition information can be used to identify the same dentition.

The Rise and Efficiency of Low Field Portable MRI Scanners

AbstractSince the discovery of magnetic resonance imaging (MRI) as an imaging modality, it has evolved immensely and is still doing so. Most imaging modalities have made bedside or emergency imaging possible due to their portability. This aspect is yet to be fully evaluated and established in the case of MRI as its high-field strength requires specialized infrastructure and its time-consuming nature makes its portability questionable. The goal of this review is to access the efficiency and feasibility of low-field portable MRI (pMRI) systems in a wide array of health care applications. Articles from indexed journals, on PubMed, Springer, Elsevier, etc. databases, relevant to this study were searched and reviewed. This review provides an atypical design that could be used in making a pMRI unit that could find its potential in diagnosing a wide variety of pathologies with an added advantage of imaging critical patients in the intensive care unit or patients in isolation due to its portability, imaging patients with implants or prosthesis effectively due to its low field, pediatric imaging due to its high speed, for guided interventions, imaging obese and claustrophobic patients due to its open nature, in dental imaging, extremity scanning, etc. With its vast spectrum of applications in the health care system, the future of low-field pMRI units seems to be bright.

Machine Learning in X-ray Diagnosis for Oral Health: A Review of Recent Progress

The past few decades have witnessed remarkable progress in the application of artificial intelligence (AI) and machine learning (ML) in medicine, notably in medical imaging. The application of ML to dental and oral imaging has also been developed, powered by the availability of clinical dental images. The present work aims to investigate recent progress concerning the application of ML in the diagnosis of oral diseases using oral X-ray imaging, namely the quality and outcome of such methods. The specific research question was developed using the PICOT methodology. The review was conducted in the Web of Science, Science Direct, and IEEE Xplore databases, for articles reporting the use of ML and AI for diagnostic purposes in X-ray-based oral imaging. Imaging types included panoramic, periapical, bitewing X-ray images, and oral cone beam computed tomography (CBCT). The search was limited to papers published in the English language from 2018 to 2022. The initial search included 104 papers that were assessed for eligibility. Of these, 22 were included for a final appraisal. The full text of the articles was carefully analyzed and the relevant data such as the clinical application, the ML models, the metrics used to assess their performance, and the characteristics of the datasets, were registered for further analysis. The paper discusses the opportunities, challenges, and limitations found.

The effects of technical factors on the fractal dimension in different dental radiographic images.

The aim of this study was to assess the impact of exposure parameters and image formats on fractal dimension (FD) values in periapical, panoramic, and CBCT images. Seven dry male mandibles were selected, and a Gutta-Percha was used to identify identical regions of interest. A periapical radiograph was taken with 60 kVp/7 mA and exported in DICOM, JPEG, TIFF, and PNG formats. Nine periapical radiographs (60, 65, 70 kVp; 4, 5, 6 mA) were taken from seven dry human mandibles. Additionally, 12 panoramic radiographs (60, 70, 81, 90 kVp; 5, 8, 13 mA) and 10 CBCT images (with different scanning options and FOVs) were taken from each mandible. FDs were measured from a standard area. The intra-class correlation coefficient demonstrated a high degree of agreement between observers. No significant difference was found between TIFF and PNG formats (p > 0.05). The highest FD mean was found in TIFF format, while the lowest FD mean was found in JPEG format (p<0.001). There was no significant difference between kVp and mA settings in periapical images. In panoramic images, a significant difference was found at 90 kVp (p = 0.001) and 13 mA (p<0.001), with lower FD values observed at these settings. There was no significant difference between FOV and resolution in CBCT images (p > 0.05). The format of the image can influence FD. For periapical and panoramic radiographs, kVp and mA settings do not have a significant impact on FD. However, fractal analysis may not be an ideal method for evaluating three-dimensional images, such as those obtained with CBCT.

Alveolar Bone Loss Detection and Localization in Dental X-Ray Images using YOLOv5

Periodontal disease, characterized by alveolar bone loss, is a prevalent oral health condition that requires early detection and management to prevent further progression. This paper proposes a novel approach for alveolar bone loss detection and localization in dental X-ray images using the YOLOv5 object detection algorithm. We annotated a dataset of dental radiographs with alveolar bone loss regions and fine-tuned the YOLOv5 model on this dataset. Our approach achieved high accuracy and robustness in detecting and localizing alveolar bone loss regions, with precision, recall, and F1 score exceeding 90%. The real-time processing capabilities of YOLOv5 make it suitable for clinical implementation, providing an efficient and accurate solution for periodontal disease management. The automated alveolar bone loss detection and localization using YOLOv5 can significantly assist dentists in the early diagnosis and treatment planning of periodontal diseases, leading to improved patient outcomes and reduced risks of tooth loss. The proposed method has the potential to be integrated into clinical practice, providing a valuable tool for dental practitioners in the field of periodontics.

A Benchmark Dual-Modality Dental Imaging Dataset and a Novel Cognitively Inspired Pipeline for High-Resolution Dental Point Cloud Synthesis

A Benchmark Dual-Modality Dental Imaging Dataset and a Novel Cognitively Inspired Pipeline for High-Resolution Dental Point Cloud Synthesis

Deep learning-based registration of two-dimensional dental images with edge specific loss.

Image registration is a very common procedure in dental applications for aligning images. Registration between pairs of images taken from different angles can improve diagnosis. Our study presents an edge-enhanced unsupervised deep learning (DL)-based deformable registration framework for aligning two-dimensional (2D) pairs of dental x-ray images. The proposed neural network is based on the combination of a U-Net like structure, which produces a displacement field, combined with spatial transformer networks, which produce the transformed image. The proposed structure is trained end-to-end by minimizing a weighted loss function consisting of three parts corresponding to image similarity, edge similarity, and registration restrictions. In this regard, the proposed edge specific loss enhances the unsupervised training of the registration framework without the need of supervision through anatomical structures. The proposed framework was applied to two datasets, a set of 104 x-ray images of mandibles, arranged in 2600 pairs for training and testing and a set of 17 pairs of pre- and post-operative reconstructed panoramic images. The proposed model outperformed both conventional registration methods and DL-based techniques for both qualitative and quantitative assessment, in most of the compared metrics concerning intensity similarity and edge distances. The proposed framework achieved accurate and fast deformable alignment of pairs of 2D dental radiographic images. The edge-based module of the loss function enhances the unsupervised learning by directing the network toward deformations that take into consideration the edges of the depicted objects (teeth, bone, and tissue), which are crucial in diagnosis.

Panoramic dental tomosynthesis imaging by use of CBCT projection data

Dental CBCT and panoramic images are important imaging modalities used in dental diagnosis and treatment planning. In order to acquire a panoramic image without an additional panoramic scan, in this study, we proposed a method of reconstructing a panoramic image by extracting panoramic projection data from dental CBCT projection data. After specifying the patient’s dental arch from the patient’s CBCT image, panoramic projection data are extracted from the CBCT projection data along the appropriate panoramic scan trajectory that fits the dental arch. A total of 40 clinical human datasets and one head phantom dataset were used to test the proposed method. The clinical human dataset used in this study includes cases in which it is difficult to reconstruct panoramic images from CBCT images, such as data with severe metal artifacts or data without teeth. As a result of applying the panoramic image reconstruction method proposed in this study, we were able to successfully acquire panoramic images from the CBCT projection data of various patients. The proposed method acquires a universally applicable panoramic image that is less affected by CBCT image quality and metal artifacts by extracting panoramic projection data from dental CBCT data and reconstructing a panoramic image.