Differential diagnosis of cemento-osseous dysplasia and periapical cyst using texture analysis of CBCT.

BACKGROUND: Radiolucencies found at the root apex in patients with cemento-osseous dysplasia (COD) may be mistaken for periapical cysts (PC) of endodontic origin. The purpose of this study was to examine the utility of quantitative texture analysis using cone-beam computed tomography (CBCT) to differentiate between COD and PC. METHODS: Patients who underwent CBCT at Wonkwang University Daejeon Dental Hospital between January 2019 and December 2022 and were diagnosed with COD and PC by clinical, radiologic, and, if necessary, histopathologic examination were included. Twenty-five patients each were retrospectively enrolled in the COD and PC group. All lesions observed on axial CBCT images were manually segmented using the open-access software MaZda version 4.6 to establish the regions of interest, which were then subjected to texture analysis. Among the 279 texture features obtained, 10 texture features with the highest Fisher coefficients were selected. Statistical analysis was performed using the Mann-Whitney U-test, Welch's t-test, or Student's t-test. Texture features that showed significant differences were subjected to receiver operating characteristics (ROC) curve analysis to evaluate the differential diagnostic ability of COD and PC. RESULTS: The COD group consisted of 22 men and 3 women, while the PC group consisted of 14 men and 11 women, showing a significant difference between the two groups in terms of sex (p=0.003). The 10 selected texture features belonged to the gray level co-occurrence matrix and included the sum of average, sum of entropy, entropy, and difference of entropy. All 10 selected texture features showed statistically significant differences (p<0.05) when comparing patients with COD (n=25) versus those with PC (n=25), osteolytic-stage COD (n=11) versus PC (n=25), and osteolytic-stage COD (n=11) versus cementoblastic-stage COD (n=14). ROC curve analysis to determine the ability to differentiate between COD and PC showed a high area under the curve ranging from 0.96 to 0.98. CONCLUSION: Texture analysis of CBCT images has shown good diagnostic value in the differential diagnosis of COD and PC, which can help prevent unnecessary endodontic treatment, invasive biopsy, or surgical intervention associated with increased risk of infection.

Resorptive Root Features in Mandibular Molars with Pronounced Root Divergence on Panoramic Radiographs: A Case Series.

INTRODUCTION: The visualization and understanding of the details of the root configurations and root canal structure are essential prior to root canal treatment. This study aimed to identify key indicators of pronounced root divergence between the distobuccal and distolingual roots in mandibular first molars by highlighting common features observed in panoramic radiographs. These indicators can help predict the likelihood of encountering significant root divergence before initiating endodontic treatment. CASE PRESENTATION: We present three cases in which panoramic radiographs displayed imaging features characteristic of root resorption in the distal root of the mandibular first molars. However, subsequent periapical radiographs in case 1 and cone-beam computed tomography images in cases 2 and 3 revealed that the mandibular first molars were in normal condition, with pronounced root divergence but no evidence of root resorption. CONCLUSION: Panoramic radiographs depicting mandibular molar roots with a resorptive and unclear appearance may indicate the presence of severe root divergence. In such cases, we strongly recommend additional cone-beam computed tomographic imaging to ensure precise diagnosis and facilitate optimal treatment planning for endodontic procedures.

Resolving the non-uniformity in the feature space of age estimation: A deep learning model based on feature clusters of panoramic images.

Age estimation is important in forensics, and numerous techniques have been investigated to estimate age based on various parts of the body. Among them, dental tissue is considered reliable for estimating age as it is less influenced by external factors. The advancement in deep learning has led to the development of automatic estimation of age using dental panoramic images. Typically, most of the medical datasets used for model learning are non-uniform in the feature space. This causes the model to be highly influenced by dense feature areas, resulting in adequate estimations; however, relatively poor estimations are observed in other areas. An effective solution to address this issue can be pre-dividing the data by age feature and training each regressor to estimate the age for individual features. In this study, we divide the data based on feature clusters obtained from unsupervised learning. The developed model comprises a classification head and multi-regression head, wherein the former predicts the cluster to which the data belong and the latter estimates the age within the predicted cluster. The visualization results show that the model can focus on a clinically meaningful area in each cluster for estimating age. The proposed model outperforms the models without feature clusters by focusing on the differences within the area. The performance improvement is particularly noticeable in the growth and aging periods. Furthermore, the model can adequately estimate the age even for samples with a high probability of classification error as they are located at the border of two feature clusters.

Estimating chronological age through learning local and global features of panoramic radiographs in the Korean population.

This study suggests a hybrid method based on ResNet50 and vision transformer (ViT) in an age estimation model. To this end, panoramic radiographs are used for learning by considering both local features and global information, which is important in estimating age. Transverse and longitudinal panoramic images of 9663 patients were selected (4774 males and 4889 females with a mean age of 39 years and 3 months). To compare ResNet50, ViT, and the hybrid model, the mean absolute error, mean square error, root mean square error, and coefficient of determination (R2) were used as metrics. The results confirmed that the age estimation model designed using the hybrid method performed better than those using only ResNet50 or ViT. The estimation is highly accurate for young people at an age with distinct growth characteristics. When examining the basis for age estimation in the hybrid model through attention rollout, the proposed model used logical and important factors rather than relying on unclear elements as the basis for age estimation.

Nasal cavity perforation by implant fixtures: case series with emphasis on panoramic imaging of nasal cavity extending posteriorly.

The nasal cavity is an important landmark when considering implant insertion into the anterior region of the maxillary arch. The perforation of implants into the nasal cavity may cause complications, such as implant migration, inflammation, or changes in nasal airflow; thus, precise assessment of the nasal cavity is mandatory.Three cases of nasal cavity perforation by dental implants are presented, including one case of implant fixture migration into the nasal cavity. On panoramic radiographs of the patients, the following common features were observed: the horizontal radiopaque line of the hard palate was observed to be inferior to or similar to that of the antral floor and the bone between the lateral wall of the nasal cavity and the medial wall of the maxillary sinus was emphasized in a triangular shape.When the maxillary sinus is small and alveolar bone resorption is severe, panoramic evaluation may cause overestimation of the available residual bone, particularly in the maxillary canine/premolar region. Therefore, the residual bone should be reevaluated three-dimensionally to measure the exact bony shape and volume.

Development and validation of a clinical phantom reproducing various lesions for oral and maxillofacial radiology research.

Purpose: The objective of this study was to propose a method for developing a clinical phantom to reproduce various diseases that are clinically prevalent in the field of dentistry. This could facilitate diverse clinical research without unnecessarily exposing patients to radiation. Material and Methods: This study utilized a single dry skull, which was visually and radiographically examined to evaluate its condition. Existing lesions on the dry skull were preserved, and other relevant lesions were artificially created as necessary. These lesions were then documented using intraoral radiography and cone-beam computed tomography. Once all pre-existing and reproduced lesions were confirmed by the consensus of 2 oral and maxillofacial radiologists, the skull was embedded in a soft tissue substitute. To validate the process, cone-beam computed tomography scans and panoramic radiographs were obtained of the fabricated phantom. All acquired images were subsequently evaluated. Results: Most lesions could be identified on panoramic radiographs, although some sialoliths and cracked teeth were confirmed only through cone-beam computed tomographic images. A small gap was observed between the epoxy resin and the bone structures. However, 2 oral and maxillofacial radiologists agreed that this space did not meaningfully impact the interpretation process. Conclusion: The newly developed phantom has potential for use as a standardized phantom within the dental field. It may be utilized for a variety of imaging studies, not only for optimization purposes, but also for addressing other experimental issues related to both 2- and 3-dimensional diagnostic radiography.

Correlation analysis between radiation exposure and the image quality of cone-beam computed tomography in the dental clinical environment.

Purpose: This study was conducted to measure the radiation exposure and image quality of various cone-beam computed tomography (CBCT) machines under common clinical conditions and to analyze the correlation between them. Materials and Methods: Seven CBCT machines used frequently in clinical practice were selected. Because each machine has various sizes of fields of view (FOVs), 1 large FOV and 1 small FOV were selected for each machine. Radiation exposure was measured using a dose-area product (DAP) meter. The quality of the CBCT images was analyzed using 8 image quality parameters obtained using a dental volume tomography phantom. For statistical analysis, regression analysis using a generalized linear model was used. Results: Polymethyl-methacrylate (PMMA) noise and modulation transfer function (MTF) 10% showed statistically significant correlations with DAP values, presenting positive and negative correlations, respectively (P<0.05). Image quality parameters other than PMMA noise and MTF 10% did not demonstrate statistically significant correlations with DAP values. Conclusion: As radiation exposure and image quality are not proportionally related in clinically used equipment, it is necessary to evaluate and monitor radiation exposure and image quality separately.

Mucocele in the maxillary sinus involving the orbit: A report of 2 cases.

Mucocele of the paranasal sinuses is a benign, slow-growing, expansile lesion. Maxillary sinus mucoceles are usually associated with painless bulging of the cheek; however, orbital expansion is rarely observed. Maxillary sinus mucoceles can be classified as primary or secondary according to their etiology. An impediment to sinus ostium ventilation is thought to be the cause of primary mucocele, while sequestering of residual mucosa after surgery in the wound and long-term retention of tissue fluid have been suggested to lead to the formation of secondary mucocele. This report presents 2 cases of primary and secondary mucoceles, with a focus on radiographic features. As primary and superiorly positioned secondary maxillary sinus mucoceles are uncommon and their close proximity to the orbit predisposes the patient to significant morbidity, the authors expect that this report will contribute to a better understanding and diagnosis of maxillary sinus mucocele involving the orbit.

Three-Dimensional Postoperative Results Prediction for Orthognathic Surgery through Deep Learning-Based Alignment Network.

To date, for the diagnosis of dentofacial dysmorphosis, we have relied almost entirely on reference points, planes, and angles. This is time consuming, and it is also greatly influenced by the skill level of the practitioner. To solve this problem, we wanted to know if deep neural networks could predict postoperative results of orthognathic surgery without relying on reference points, planes, and angles. We use three-dimensional point cloud data of the skull of 269 patients. The proposed method has two main stages for prediction. In step 1, the skull is divided into six parts through the segmentation network. In step 2, three-dimensional transformation parameters are predicted through the alignment network. The ground truth values of transformation parameters are calculated through the iterative closest points (ICP), which align the preoperative part of skull to the corresponding postoperative part of skull. We compare pointnet, pointnet++ and pointconv for the feature extractor of the alignment network. Moreover, we design a new loss function, which considers the distance error of transformed points for a better accuracy. The accuracy, mean intersection over union (mIoU), and dice coefficient (DC) of the first segmentation network, which divides the upper and lower part of skull, are 0.9998, 0.9994, and 0.9998, respectively. For the second segmentation network, which divides the lower part of skull into 5 parts, they were 0.9949, 0.9900, 0.9949, respectively. The mean absolute error of transverse, anterior-posterior, and vertical distance of part 2 (maxilla) are 0.765 mm, 1.455 mm, and 1.392 mm, respectively. For part 3 (mandible), they were 1.069 mm, 1.831 mm, and 1.375 mm, respectively, and for part 4 (chin), they were 1.913 mm, 2.340 mm, and 1.257 mm, respectively. From this study, postoperative results can now be easily predicted by simply entering the point cloud data of computed tomography.

McCune-Albright syndrome with acromegaly: A case report with characteristic radiographic features of fibrous dysplasia.

McCune-Albright syndrome (MAS) is a rare multisystem disorder characterized by a clinical triad of polyostotic fibrous dysplasia (FD), skin pigmentation, and hyperfunctioning endocrinopathies. A 42-year-old man visited our medical hospital for the treatment of intermittent headaches and was diagnosed with MAS with acromegaly. This patient showed various clinical features of MAS, including pituitary adenoma, polyostotic FD, and hypogonadotropic hypogonadism. The FD lesions showed characteristic radiographic features, such as widespread, sclerotic bony lesions in the cranial bones, mixed radiolucent-radiopaque multilocular lesions in the mandible, and radiolucent lesions in the axial and appendicular skeleton. Over the years, the patient had been hospitalized multiple times due to accidental bony fractures associated with the fragile bony state of FD. This report presents a retrospective description of a case of MAS, with a review of the relevant literature.

Deep Learning-Based Prediction of Paresthesia after Third Molar Extraction: A Preliminary Study.

The purpose of this study was to determine whether convolutional neural networks (CNNs) can predict paresthesia of the inferior alveolar nerve using panoramic radiographic images before extraction of the mandibular third molar. The dataset consisted of a total of 300 preoperative panoramic radiographic images of patients who had planned mandibular third molar extraction. A total of 100 images taken of patients who had paresthesia after tooth extraction were classified as Group 1, and 200 images taken of patients without paresthesia were classified as Group 2. The dataset was randomly divided into a training and validation set (n = 150 [50%]), and a test set (n = 150 [50%]). CNNs of SSD300 and ResNet-18 were used for deep learning. The average accuracy, sensitivity, specificity, and area under the curve were 0.827, 0.84, 0.82, and 0.917, respectively. This study revealed that CNNs can assist in the prediction of paresthesia of the inferior alveolar nerve after third molar extraction using panoramic radiographic images.

Nasopalatine duct cyst with sebaceous differentiation: a rare case report with literature review.

BACKGROUND: The aim of this study was to report a rare case of nasopalatine duct cyst with sebaceous differentiation. Further, a systematic search of the literature was performed to identify studies reporting patients with intraosseous jaw cysts with sebaceous differentiation. CASE PRESENTATION: A 55-year-old Korean man was referred to our hospital because of a cystic lesion of the anterior maxilla. Radiologic examination revealed a well-circumscribed radiolucent lesion in the anterior maxilla. Histology showed a respiratory columnar and cuboidal epithelium-lined cyst. Transition from the ciliated columnar epithelium to stratified squamous epithelium with sebaceous differentiation was observed. Based on these findings, the final diagnosis was nasopalatine duct cyst with sebaceous differentiation. A systematic search of the literature was performed to identify studies reporting patients with intraosseous jaw cysts with sebaceous differentiation. There were 24 cases of sebaceous differentiation in the epithelium of the cysts including 2 odontogenic keratocysts, 8 orthokeratinized odontogenic cysts, 8 dentigerous cysts, 1 radicular cyst, and 2 glandular odontogenic cysts. However, no case reports describing the occurrence of nasopalatine duct cysts with sebaceous differentiation have been reported. CONCLUSION: This first case report of nasopalatine duct cysts with sebaceous differentiation could provide insight into the diagnostic process of cystic lesions with sebaceous differentiation.

Deep-Learning-Based Detection of Cranio-Spinal Differences between Skeletal Classification Using Cephalometric Radiography.

The aim of this study was to reveal cranio-spinal differences between skeletal classification using convolutional neural networks (CNNs). Transverse and longitudinal cephalometric images of 832 patients were used for training and testing of CNNs (365 males and 467 females). Labeling was performed such that the jawbone was sufficiently masked, while the parts other than the jawbone were minimally masked. DenseNet was used as the feature extractor. Five random sampling crossvalidations were performed for two datasets. The average and maximum accuracy of the five crossvalidations were 90.43% and 92.54% for test 1 (evaluation of the entire posterior-anterior (PA) and lateral cephalometric images) and 88.17% and 88.70% for test 2 (evaluation of the PA and lateral cephalometric images obscuring the mandible). In this study, we found that even when jawbones of class I (normal mandible), class II (retrognathism), and class III (prognathism) are masked, their identification is possible through deep learning applied only in the cranio-spinal area. This suggests that cranio-spinal differences between each class exist.

Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals.

BACKGROUND: Posteroanterior and lateral cephalogram have been widely used for evaluating the necessity of orthognathic surgery. The purpose of this study was to develop a deep learning network to automatically predict the need for orthodontic surgery using cephalogram. METHODS: The cephalograms of 840 patients (Class ll: 244, Class lll: 447, Facial asymmetry: 149) complaining about dentofacial dysmorphosis and/or a malocclusion were included. Patients who did not require orthognathic surgery were classified as Group I (622 patients-Class ll: 221, Class lll: 312, Facial asymmetry: 89). Group II (218 patients-Class ll: 23, Class lll: 135, Facial asymmetry: 60) was set for cases requiring surgery. A dataset was extracted using random sampling and was composed of training, validation, and test sets. The ratio of the sets was 4:1:5. PyTorch was used as the framework for the experiment. RESULTS: Subsequently, 394 out of a total of 413 test data were properly classified. The accuracy, sensitivity, and specificity were 0.954, 0.844, and 0.993, respectively. CONCLUSION: It was found that a convolutional neural network can determine the need for orthognathic surgery with relative accuracy when using cephalogram.

Deep learning based prediction of extraction difficulty for mandibular third molars.

This paper proposes a convolutional neural network (CNN)-based deep learning model for predicting the difficulty of extracting a mandibular third molar using a panoramic radiographic image. The applied dataset includes a total of 1053 mandibular third molars from 600 preoperative panoramic radiographic images. The extraction difficulty was evaluated based on the consensus of three human observers using the Pederson difficulty score (PDS). The classification model used a ResNet-34 pretrained on the ImageNet dataset. The correlation between the PDS values determined by the proposed model and those measured by the experts was calculated. The prediction accuracies for C1 (depth), C2 (ramal relationship), and C3 (angulation) were 78.91%, 82.03%, and 90.23%, respectively. The results confirm that the proposed CNN-based deep learning model could be used to predict the difficulty of extracting a mandibular third molar using a panoramic radiographic image.

Deep-learning for predicting C-shaped canals in mandibular second molars on panoramic radiographs.

OBJECTIVE: The aim of this study was to evaluate the use of a convolutional neural network (CNN) system for predicting C-shaped canals in mandibular second molars on panoramic radiographs. METHODS: Panoramic and cone beam CT (CBCT) images obtained from June 2018 to May 2020 were screened and 1020 patients were selected. Our dataset of 2040 sound mandibular second molars comprised 887 C-shaped canals and 1153 non-C-shaped canals. To confirm the presence of a C-shaped canal, CBCT images were analyzed by a radiologist and set as the gold standard. A CNN-based deep-learning model for predicting C-shaped canals was built using Xception. The training and test sets were set to 80 to 20%, respectively. Diagnostic performance was evaluated using accuracy, sensitivity, specificity, and precision. Receiver-operating characteristics (ROC) curves were drawn, and the area under the curve (AUC) values were calculated. Further, gradient-weighted class activation maps (Grad-CAM) were generated to localize the anatomy that contributed to the predictions. RESULTS: The accuracy, sensitivity, specificity, and precision of the CNN model were 95.1, 92.7, 97.0, and 95.9%, respectively. Grad-CAM analysis showed that the CNN model mainly identified root canal shapes converging into the apex to predict the C-shaped canals, while the root furcation was predominantly used for predicting the non-C-shaped canals. CONCLUSIONS: The deep-learning system had significant accuracy in predicting C-shaped canals of mandibular second molars on panoramic radiographs.

Deep learning based discrimination of soft tissue profiles requiring orthognathic surgery by facial photographs.

Facial photographs of the subjects are often used in the diagnosis process of orthognathic surgery. The aim of this study was to determine whether convolutional neural networks (CNNs) can judge soft tissue profiles requiring orthognathic surgery using facial photographs alone. 822 subjects with dentofacial dysmorphosis and / or malocclusion were included. Facial photographs of front and right side were taken from all patients. Subjects who did not need orthognathic surgery were classified as Group I (411 subjects). Group II (411 subjects) was set up for cases requiring surgery. CNNs of VGG19 was used for machine learning. 366 of the total 410 data were correctly classified, yielding 89.3% accuracy. The values of accuracy, precision, recall, and F1 scores were 0.893, 0.912, 0.867, and 0.889, respectively. As a result of this study, it was found that CNNs can judge soft tissue profiles requiring orthognathic surgery relatively accurately with the photographs alone.

Concomitant cemento-osseous dysplasia and aneurysmal bone cyst of the mandible: a rare case report with literature review.

BACKGROUND: Concomitant cemento-osseous dysplasia (COD) and aneurysmal bone cyst (ABC) are rare in the head and neck region. In our search of the English language literature, we found only one case report describing the simultaneous occurrence of COD and ABC in the head and neck region. Here, we report a case of COD associated with ABC. Further, we performed a systematic search of the literature to identify studies on patients with COD associated with nonepithelial lined cysts of the jaws. CASE PRESENTATION: The patient was a 32-year-old woman who was referred from a private dental clinic because of a cystic lesion below the mandibular right first molar. She had no pain or significant systemic disease. After performing panoramic radiography and cone-beam computed tomography, the imaging diagnosis was COD with a cystic lesion, such as ABC or solitary bone cyst. Excisional biopsy was performed, which revealed concomitant COD and ABC. CONCLUSION: This case of ABC associated with COD provides insight for the diagnostic process of radiographically mixed lesions with cystic changes.

Development of panorama resolution phantom for comprehensive evaluation of the horizontal and vertical resolution of panoramic radiography.

Panoramic radiography is the most commonly used equipment in the dental field, but there is no comprehensive standard about how to evaluate the spatial resolution of panoramic radiography. In this study, panorama resolution phantoms were developed for evaluation of horizontal and vertical resolution reflecting unique characteristics of panoramic radiography. Four horizontal resolution phantoms of staircase shape were designed to obtain images of horizontal lead line pairs in a 52 mm width. Four vertical resolution phantoms with vertical lead line pairs placed at an oblique angle were also designed. A phantom stand was made. Three machines were evaluated twice by two oromaxillofacial radiologists. The horizontal lead line pairs were readable over the entire measured area at the values of 1.88, 2.32, and 2.58 lp/mm for all machines. A readable area of horizontal 3.19 lp/mm was observed in the lingual side. In the vertical resolution phantoms, it was possible to read a narrower range. By using the panorama resolution phantoms and phantom stand, it was possible to evaluate the resolution of a wide buccolingual width in four significant areas. By evaluating the resolution of the vertical and horizontal compartments separately, it was possible to gain a better understanding of the obtained images.

Correlation between spatial resolution and ball distortion rate of panoramic radiography.

BACKGROUND: The purpose of this study was to analyze the correlation between spatial resolution and ball distortion rate of panoramic radiography and to elucidate the minimum criterion for ball distortion rate, which is very relevant to clinical readability. METHODS: Horizontal and vertical spatial resolution and ball distortion rates were calculated in the same position, such as the incisor, premolar, molar, and temporomandibular joint area with various object depths corresponding to 48 mm. Three devices were evaluated. A region showing spatial resolution above the reference standard was selected, and the ball distortion rate corresponding to the same region was divided into horizontal and vertical phantom groups. The mean and standard deviation of the obtained ball distortion rates were calculated. Student's t-test was used to statistically analyze the mean difference in ball distortion rates between vertical and horizontal phantom groups. RESULTS: In all devices, the horizontal line pair phantom, but not the vertical line pair phantom, was readable in all areas measured at the line pair value of at least 1.88 lp/mm. The line pair value tended to be higher toward the center and lower toward the outside. The ball distortion rate tended to decrease closer to the center and increased further away. In addition, ball distortion rates could not be measured at some areas as they were not recognized as balls due to the high degree of distortion at the outermost and innermost sides. The number of balls satisfying the reference value using the horizontal line pair phantom was 102 (mean of ball distortion rates, 20.98; standard deviation, 15.25). The number of balls satisfying the reference value using the vertical line pair phantom was 49 (mean of ball distortion rates, 16.33; standard deviation, 14.25). However, mean ball distortion rate was not significantly different between the two groups. CONCLUSIONS: Image layer of panoramic radiography could be evaluated by the spatial resolution using horizontal and vertical line pair phantoms and by assessing ball distortion rates through a ball-type panorama phantom. A ball distortion rate of 20% could be used as a threshold to evaluate the image layer of panoramic radiography.