Application of panoramic radiography with a multilayer imaging program for detecting proximal caries: a preliminary clinical study

Radiographic diagnosis of periodontal diseases - Current evidence versus innovations

Accurate diagnosis of periodontal and peri-implant diseases relies significantly on radiographic examination, especially for assessing alveolar bone levels, bone defect morphology, and bone quality. This narrative review aimed to comprehensively outline the current state-of-the-art in radiographic diagnosis of alveolar bone diseases, covering both two-dimensional (2D) and three-dimensional (3D) modalities. Additionally, this review explores recent technological advances in periodontal imaging diagnosis, focusing on their potential integration into clinical practice. Clinical probing and intraoral radiography, while crucial, encounter limitations in effectively assessing complex periodontal bone defects. Recognizing these challenges, 3D imaging modalities, such as cone beam computed tomography (CBCT), have been explored for a more comprehensive understanding of periodontal structures. The significance of the radiographic assessment approach is evidenced by its ability to offer an objective and standardized means of evaluating hard tissues, reducing variability associated with manual clinical measurements and contributing to a more precise diagnosis of periodontal health. However, clinicians should be aware of challenges related to CBCT imaging assessment, including beam-hardening artifacts generated by the high-density materials present in the field of view, which might affect image quality. Integration of digital technologies, such as artificial intelligence-based tools in intraoral radiography software, the enhances the diagnostic process. The overarching recommendation is a judicious combination of CBCT and digital intraoral radiography for enhanced periodontal bone assessment. Therefore, it is crucial for clinicians to weigh the benefits against the risks associated with higher radiation exposure on a case-by-case basis, prioritizing patient safety and treatment outcomes.

Reduction of cervical vertebra ghost images in panoramic radiography using vertical dual exposure

PURPOSE

To evaluate the image quality of vertical dual-exposure panoramic radiography (PR), which merges two PR images taken at different focus heights to reduce ghost images of cervical vertebrae (CV) and intervertebral spaces (IVS) in the incisor region.

METHODS

PR images of an aluminum block, a CV phantom and a human head phantom were taken at 0 mm and merged with and subtracted from PR images taken at other heights (0, 5, 10, 15, and 20 mm) to create new images, e.g., Merg0 + 15 mm and Sub0 - 10 mm. The subtracted images were analyzed subjectively according to the uniformity on the line profile. Merged images were evaluated subjectively by six raters to determine the influence of the ghost images.

RESULTS

Objective evaluation revealed a positional shift in the ghost images according to the height of the focus for both phantoms. In the subjective evaluation, the normal PR (Merg0 + 0 mm) showed the worst score, indicating strong influence of CV and IVS ghost images.

CONCLUSION

The vertical dual-exposure PR method, which merges PR images taken at the normal position and a higher X-ray focus, can reduce CV and IVS ghost images in the incisor region.

Evaluation of a Decision Support System Developed with Deep Learning Approach for Detecting Dental Caries with Cone-Beam Computed Tomography Imaging

This study aims to investigate the effect of using an artificial intelligence (AI) system (Diagnocat, Inc., San Francisco, CA, USA) for caries detection by comparing cone-beam computed tomography (CBCT) evaluation results with and without the software. 500 CBCT volumes are scored by three dentomaxillofacial radiologists for the presence of caries separately on a five-point confidence scale without and with the aid of the AI system. After visual evaluation, the deep convolutional neural network (CNN) model generated a radiological report and observers scored again using AI interface. The ground truth was determined by a hybrid approach. Intra- and inter-observer agreements are evaluated with sensitivity, specificity, accuracy, and kappa statistics. A total of 6008 surfaces are determined as 'presence of caries' and 13,928 surfaces are determined as 'absence of caries' for ground truth. The area under the ROC curve of observer 1, 2, and 3 are found to be 0.855/0.920, 0.863/0.917, and 0.747/0.903, respectively (unaided/aided). Fleiss Kappa coefficients are changed from 0.325 to 0.468, and the best accuracy (0.939) is achieved with the aided results. The radiographic evaluations performed with aid of the AI system are found to be more compatible and accurate than unaided evaluations in the detection of dental caries with CBCT images.

Refinement of image quality in panoramic radiography using a generative adversarial network

OBJECTIVE

We aimed to develop and assess the clinical usefulness of a generative adversarial network (GAN) model for improving image quality in panoramic radiography.

METHODS

Panoramic radiographs obtained at Yonsei University Dental Hospital were randomly selected for study inclusion (n = 100). Datasets with degraded image quality (n = 400) were prepared using four different processing methods: blur, noise, blur with noise, and blur in the anterior teeth region. The images were distributed to the training and test datasets in a ratio of 9:1 for each group. The Pix2Pix GAN model was trained using pairs of the original and degraded image datasets for 100 epochs. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were obtained for the test dataset, and two oral and maxillofacial radiologists rated the quality of clinical images.

RESULTS

Among the degraded images, the GAN model enabled the greatest improvement in those with blur in the region of the anterior teeth but was least effective in improving images exhibiting blur with noise (PSNR, 36.27 > 32.74; SSIM, 0.90 > 0.82). While the mean clinical image quality score of the original radiographs was 44.6 out of 46.0, the highest and lowest predicted scores were observed in the blur (45.2) and noise (36.0) groups.

CONCLUSION

The GAN model developed in this study has the potential to improve panoramic radiographs with degraded image quality, both quantitatively and qualitatively. As the model performs better in refining blurred images, further research is required to identify the most effective methods for handling noisy images.

Tooth Type Enhanced Transformer for Children Caries Diagnosis on Dental Panoramic Radiographs

The objective of this study was to introduce a novel deep learning technique for more accurate children caries diagnosis on dental panoramic radiographs. Specifically, a swin transformer is introduced, which is compared with the state-of-the-art convolutional neural network (CNN) methods that are widely used for caries diagnosis. A tooth type enhanced swin transformer is further proposed by considering the differences among canine, molar and incisor. Modeling the above differences in swin transformer, the proposed method was expected to mine domain knowledge for more accurate caries diagnosis. To test the proposed method, a children panoramic radiograph database was built and labeled with a total of 6028 teeth. Swin transformer shows better diagnosis performance compared with typical CNN methods, which indicates the usefulness of this new technique for children caries diagnosis on panoramic radiographs. Furthermore, the proposed tooth type enhanced swin transformer outperforms the naive swin transformer with the accuracy, precision, recall, F1 and area-under-the-curve being 0.8557, 0.8832, 0.8317, 0.8567 and 0.9223, respectively. This indicates that the transformer model can be further improved with a consideration of domain knowledge instead of a copy of previous transformer models designed for natural images. Finally, we compare the proposed tooth type enhanced swin transformer with two attending doctors. The proposed method shows higher caries diagnosis accuracy for the first and second primary molars, which may assist dentists in caries diagnosis.

Frequency of Dental X-ray Diagnostics in Children and Adolescents: What Is the Radiation Exposure?

Children are exposed to ionizing radiation through radiographs during their development for various reasons. At present, there are no officially valid reference values for dental X-rays in children and adolescents for dental X-ray diagnostics. This study retrospectively examined 9680 extraoral dental radiographs in pediatric patients between 2002 and 2020. The aim was to analyze the radiation doses in pediatric patients, which indications were used, and whether there were specific age and gender differences. The evaluation showed that radiation doses were considered low, with dose area products of 2.2 cGy × cm2 for a lateral cephalogram, 14 cGy × cm2 for an orthopantomogram (OPG), and 45 cGy × cm2 for cone beam computer tomography (CBCT). This corresponds to an effective dose of 1.5 μSv for a lateral cephalogram, 7 μSv for an OPG, and 33.8 μSv for CBCT. Of the 9680 images, 78% were orthopantomograms, and only 0.4% were CBCT images. OPG has become more important over the years, as reflected in the indication. Approximately one-third of all extraoral exposures are orthodontic indications. Overall, the indications were similar for both genders. According to the dental indications, boys were X-rayed slightly more frequently than girls (54.5-45.5%). A future publication of dose guide values and corresponding guidelines is of high priority.

Context Aware Convolutional Neural Network for Children Caries Diagnosis on Dental Panoramic Radiographs

The objective of this study is to improve traditional convolutional neural networks for more accurate children dental caries diagnosis on panoramic radiographs. A context aware convolutional neural network (CNN) is proposed by considering information among adjacent teeth, based on the fact that caries of teeth often affects each other due to the same growing environment. Specifically, when performing caries diagnosis on a tooth, information from its adjacent teeth will be collected and adaptively fused for final classification. Children panoramic radiographs of 210 patients with one or more caries and 94 patients without caries are utilized, among which there are a total of 6028 teeth with 3039 to be caries. The proposed context aware CNN outperforms typical CNN baseline with the accuracy, precision, recall, F1 score, and area-under-the-curve (AUC) being 0.8272, 0.8538, 0.8770, 0.8652, and 0.9005, respectively, showing potential to improve typical CNN instead of just copying them in previous works. Specially, the proposed method performs better than two five-year attending doctors for the second primary molar caries diagnosis. Considering the results obtained, it is beneficial to promote CNN based deep learning methods for assisting dentists for caries diagnosis in hospitals.

Automatic detection of mesiodens on panoramic radiographs using artificial intelligence

This study aimed to develop an artificial intelligence model that can detect mesiodens on panoramic radiographs of various dentition groups. Panoramic radiographs of 612 patients were used for training. A convolutional neural network (CNN) model based on YOLOv3 for detecting mesiodens was developed. The model performance according to three dentition groups (primary, mixed, and permanent dentition) was evaluated, both internally (130 images) and externally (118 images), using a multi-center dataset. To investigate the effect of image preprocessing, contrast-limited histogram equalization (CLAHE) was applied to the original images. The accuracy of the internal test dataset was 96.2% and that of the external test dataset was 89.8% in the original images. For the primary, mixed, and permanent dentition, the accuracy of the internal test dataset was 96.7%, 97.5%, and 93.3%, respectively, and the accuracy of the external test dataset was 86.7%, 95.3%, and 86.7%, respectively. The CLAHE images yielded less accurate results than the original images in both test datasets. The proposed model showed good performance in the internal and external test datasets and had the potential for clinical use to detect mesiodens on panoramic radiographs of all dentition types. The CLAHE preprocessing had a negligible effect on model performance.

Mapping of a multilayer panoramic radiography device

OBJECTIVE

To map the shape, location, and thickness of the focal trough of a panoramic radiography device with a multilayer imaging program.

METHODS

An acrylic plate (148 × 148 × 3 mm) containing 1156 holes distributed in a matrix of 34 × 34 rows was placed in the OP300 Maxio at the levels of the maxilla and mandible. 20 metal spheres (3.5 mm in diameter) were placed on the holes of the plate under 15 different arrangements and panoramic images were acquired for each arrangement at 66 kV, 8 mA, and an exposure time of 16 s. The resulting panoramic radiographs from the five image layers were exported, the horizontal and vertical dimensions of the metal spheres were measured in all images using the Image J software, and the magnification and distortion rates of the spheres were calculated. All metal spheres presenting a magnification rate lower than 30% in both vertical and horizontal dimensions and a distortion rate lower than 10% were considered to map the focal troughs of each of the five image layers.

RESULTS

All panoramic image layers had a curved shape ranging from 39° to 51° for both dental arches and varied in position and thickness. The anterior region of maxilla was anteriorly displaced when compared to the anterior region of the mandible for all layers. Image layers are thicker at the level of the mandible than those at the level of the maxilla; also, inner layers were thinner and outer layers were thicker.

CONCLUSION

All image layers in the studied panoramic radiography device had a curved shape and varied in position and thickness. The anterior region of maxilla was anteriorly displaced when compared to that of the mandible for all layers.

Endodontic treatment of various palatal root in maxillary molars: Case series and clinical experience

BACKGROUND AND OVERVIEW

The purpose of this article is to present the variations in maxillary molar palatal root canals and provide a reference for the possible variations in root canal treatment.

CASE DESCRIPTION

Five rare cases with palatal canal variation presented in this case series received nonsurgical endodontic treatment successfully. These case reports highlight that understanding and managing the different types of canal configurations in palatal roots of maxillary molars is essential to successful root canal treatment. We tried 2 methods of examining the palatal canal variation to provide examples for clinicians in diagnosing and treating similar cases.

CONCLUSIONS AND PRACTICAL IMPLICATIONS

The outline form of the access cavity and the shape of the pulp chamber floor are important factors for identifying variations in root canal number. Moreover, cone-beam computed tomography can help in detecting variations in root canals.

Radiographic modalities for diagnosis of caries in a historical perspective: from film to machine-intelligence supported systems

Radiographic imaging for the diagnosis of caries lesions has been a supplement to clinical examination for approximately a century. Various methods, and particularly X-ray receptors, have been developed over the years, and computer systems have focused on aiding the dentist in the detection of lesions and in estimating lesion depth. The present historical review has sampled accuracy ex vivo studies and clinical studies on radiographic caries diagnosis that have compared two or more receptors for capturing the image. The epochs of film radiography, xeroradiography, digital intraoral radiography, panoramic radiography and other extraoral methods, TACT analysis, cone-beam CT and artificial intelligence systems aiding in decision-making are reviewed. The author of this review (43 years in academia) has been involved in caries research and contributed to the literature in all the mentioned epochs.