Deep Convolutional Neural Network for Automated Staging of Periodontal Bone Loss Severity on Bite-wing Radiographs: An Eigen-CAM Explainability Mapping Approach.

Periodontal disease is a significant global oral health problem. Radiographic staging is critical in determining periodontitis severity and treatment requirements. This study aims to automatically stage periodontal bone loss using a deep learning approach using bite-wing images. A total of 1752 bite-wing images were used for the study. Radiological examinations were classified into 4 groups. Healthy (normal), no bone loss; stage I (mild destruction), bone loss in the coronal third (< 15%); stage II (moderate destruction), bone loss is in the coronal third and from 15 to 33% (15-33%); stage III-IV (severe destruction), bone loss extending from the middle third to the apical third with furcation destruction (> 33%). All images were converted to 512 × 400 dimensions using bilinear interpolation. The data was divided into 80% training validation and 20% testing. The classification module of the YOLOv8 deep learning model was used for the artificial intelligence-based classification of the images. Based on four class results, it was trained using fivefold cross-validation after transfer learning and fine tuning. After the training, 20% of test data, which the system had never seen, were analyzed using the artificial intelligence weights obtained in each cross-validation. Training and test results were calculated with average accuracy, precision, recall, and F1-score performance metrics. Test images were analyzed with Eigen-CAM explainability heat maps. In the classification of bite-wing images as healthy, mild destruction, moderate destruction, and severe destruction, training performance results were 86.100% accuracy, 84.790% precision, 82.350% recall, and 84.411% F1-score, and test performance results were 83.446% accuracy, 81.742% precision, 80.883% recall, and 81.090% F1-score. The deep learning model gave successful results in staging periodontal bone loss in bite-wing images. Classification scores were relatively high for normal (no bone loss) and severe bone loss in bite-wing images, as they are more clearly visible than mild and moderate damage.

Automatic segmentation of the maxillary sinus on cone beam computed tomographic images with U-Net deep learning model.

BACKGROUND: Medical imaging segmentation is the use of image processing techniques to expand specific structures or areas in medical images. This technique is used to separate and display different textures or shapes in an image. The aim of this study is to develop a deep learning-based method to perform maxillary sinus segmentation using cone beam computed tomography (CBCT) images. The proposed segmentation method aims to provide better image guidance to surgeons and specialists by determining the boundaries of the maxillary sinus cavities. In this way, more accurate diagnoses can be made and surgical interventions can be performed more successfully. METHODS: In the study, axial CBCT images of 100 patients (200 maxillary sinuses) were used. These images were marked to identify the maxillary sinus walls. The marked regions are masked for use in the maxillary sinus segmentation model. U-Net, one of the deep learning methods, was used for segmentation. The training process was carried out for 10 epochs and 100 iterations per epoch. The epoch and iteration numbers in which the model showed maximum success were determined using the early stopping method. RESULTS: After the segmentation operations performed with the U-Net model trained using CBCT images, both visual and numerical results were obtained. In order to measure the performance of the U-Net model, IoU (Intersection over Union) and F1 Score metrics were used. As a result of the tests of the model, the IoU value was found to be 0.9275 and the F1 Score value was 0.9784. CONCLUSION: The U-Net model has shown high success in maxillary sinus segmentation. In this way, fast and highly accurate evaluations are possible, saving time by reducing the workload of clinicians and eliminating subjective errors.

AI-Driven localization of all impacted teeth and prediction of winter angulation for third molars on panoramic radiographs: Clinical user interface design.

PURPOSE: Impacted teeth are abnormal tooth disorders under the gums or jawbone that cannot take their normal position even though it is time to erupt. This study aims to detect all impacted teeth and to classify impacted third molars according to the Winter method with an artificial intelligence model on panoramic radiographs. METHODS: In this study, 1197 panoramic radiographs from the dentistry faculty database were collected for all impacted teeth, and 1000 panoramic radiographs were collected for Winter classification. Some pre-processing methods were performed and the images were doubled with data augmentation. Both datasets were randomly divided into 80% training, 10% validation, and 10% testing. After transfer learning and fine-tuning processes, the two datasets were trained with the YOLOv8 deep learning algorithm, a high-performance artificial intelligence model, and the detection of impacted teeth was carried out. The results were evaluated with precision, recall, mAP, and F1-score performance metrics. A graphical user interface was designed for clinical use with the artificial intelligence weights obtained as a result of the training. RESULTS: For the detection of impacted third molar teeth according to Winter classification, the average precision, average recall, and average F1 score were obtained to be 0.972, 0.967, and 0.969, respectively. For the detection of all impacted teeth, the average precision, average recall, and average F1 score were obtained as 0.991, 0.995, and 0.993, respectively. CONCLUSION: According to the results, the artificial intelligence-based YOLOv8 deep learning model successfully detected all impacted teeth and the impacted third molar teeth according to the Winter classification system.

AI-Assisted Detection of Interproximal, Occlusal, and Secondary Caries on Bite-Wing Radiographs: A Single-Shot Deep Learning Approach.

Tooth decay is a common oral disease worldwide, but errors in diagnosis can often be made in dental clinics, which can lead to a delay in treatment. This study aims to use artificial intelligence (AI) for the automated detection and localization of secondary, occlusal, and interproximal (D1, D2, D3) caries types on bite-wing radiographs. The eight hundred and sixty bite-wing radiographs were collected from the School of Dentistry database. Pre-processing and data augmentation operations were performed. Interproximal (D1, D2, D3), secondary, and occlusal caries on bite-wing radiographs were annotated by two oral radiologists. The data were split into 80% for training, 10% for validation, and 10% for testing. The AI-based training process was conducted using the YOLOv8 algorithm. A clinical decision support system interface was designed using the Python PyQT5 library, allowing for the use of dental caries detection without the need for complex programming procedures. In the test images, the average precision, average sensitivity, and average F1 score values for secondary, occlusal, and interproximal caries were obtained as 0.977, 0.932, and 0.954, respectively. The AI-based dental caries detection system yielded highly successful results in the test, receiving full approval from dentists for clinical use. YOLOv8 has the potential to increase sensitivity and reliability while reducing the burden on dentists and can prevent diagnostic errors in dental clinics.

Performance evaluation of a deep learning model for automatic detection and localization of idiopathic osteosclerosis on dental panoramic radiographs.

Idiopathic osteosclerosis (IO) are focal radiopacities of unknown etiology observed in the jaws. These radiopacities are incidentally detected on dental panoramic radiographs taken for other reasons. In this study, we investigated the performance of a deep learning model in detecting IO using a small dataset of dental panoramic radiographs with varying contrasts and features. Two radiologists collected 175 IO-diagnosed dental panoramic radiographs from the dental school database. The dataset size is limited due to the rarity of IO, with its incidence in the Turkish population reported as 2.7% in studies. To overcome this limitation, data augmentation was performed by horizontally flipping the images, resulting in an augmented dataset of 350 panoramic radiographs. The images were annotated by two radiologists and divided into approximately 70% for training (245 radiographs), 15% for validation (53 radiographs), and 15% for testing (52 radiographs). The study employing the YOLOv5 deep learning model evaluated the results using precision, recall, F1-score, mAP (mean Average Precision), and average inference time score metrics. The training and testing processes were conducted on the Google Colab Pro virtual machine. The test process's performance criteria were obtained with a precision value of 0.981, a recall value of 0.929, an F1-score value of 0.954, and an average inference time of 25.4 ms. Although radiographs diagnosed with IO have a small dataset and exhibit different contrasts and features, it has been observed that the deep learning model provides high detection speed, accuracy, and localization results. The automatic identification of IO lesions using artificial intelligence algorithms, with high success rates, can contribute to the clinical workflow of dentists by preventing unnecessary biopsy procedure.

Evaluation of cortical and trabecular bone structure of the mandible in patients with ankylosing spondylitis.

This study aimed to examine the difference between the fractal dimension (FD) values of the mandibular trabecular bone and the panoramic mandibular index (PMI), mandibular cortical index (MCI) and mandibular cortical thickness (MCW) of patients with ankylosing spondylitis (AS) and healthy control group. A total of 184 individuals (92 cases, 92 controls), were examined in our study. PMI, MCI, and MCW values were calculated on panoramic images of all individuals. For FD values, the region of interest (ROI) was selected with the size of 100 × 100 pixels from the right-left gonial and interdental regions and 50 × 50 pixels from the condylar region. Degenerative changes in the temporomandibular joint (TMJ) region were recorded. PMI, MCI, and MCW values showed statistically significant differences between the groups (p = 0.000, p < 0.001). The radiological signs of mandibular cortical resorption were more severe in the case group than in the control group. PMI and MCW values were found to be lower in the case group than in the control group. It was determined that the number of C3 and C2 values, among the MCI values, was higher in the case group. Only the FD values of the ROI selected from the condyle region were found to be statistically significant and were lower in the case group (p = 0.026, p < 0.05). Degenerative changes in the TMJ region were significantly more frequent in the case groups (p = 0.000, p < 0.001). The fact that the mandibular cortex shows more resorptive features in individuals with AS may require further evaluation in terms of osteoporosis. Because of the low FD values of the condylar regions of these patients and the more frequent degenerative changes, the TMJ region should be followed carefully. Detailed examination of the mandibular cortex and condylar region is beneficial in patients with AS for screening and following osteoporotic changes in these individuals, which is essential for the patient's life quality.

Evaluation of cortical and trabecular bone structure of the mandible in patients using L-Thyroxine.

BACKGROUND: Long-term use of L-Thyroxine (LT4), the synthetic thyroxine hormone used for thyroid hormone replacement therapy, is an important risk factor for osteoporosis. The aim of this study was to investigate the differences between mandibular cortical index (MCI) and trabecular bone fractal dimension (FD) values on panoramic radiographs of patients using LT4 and control subjects. METHODS: A total of 142 female patients, 71 cases and 71 controls, were analyzed in the study. Ages were matched in case and control groups and the mean age was 36.6 ± 8.2 (18 to 50) years. MCI consisting of C1 (Normal Mandibular Cortex), C2 (Moderately Resorbed Mandibular Cortex) and, C3 (Severely Resorbed Cortex) scores was determined for case and control groups. Fractal analysis was performed using ImageJ on selected regions of interest from the gonial and interdental regions. The box-count method was used to calculate FD values. Wilcoxon signed-rank test, Mann-Whitney U test, and Spearman correlation analysis were applied to compare the measurements. Statistical significance of differences was established at P < 0.05 level. RESULTS: FD values did not show statistically significant differences between case and control groups (p > 0.05). The mean FD in the right gonial region was 1.38 ± 0.07 in the case group and 1.38 ± 0.08 in the control group (p = 0.715). The mean FD in the right interdental region was 1.37 ± 0.06 in the cases and 1.36 ± 0.06 in the control group (p = 0.373). The mean FD in the left gonial region was 1.39 ± 0.07 in the cases and 1.39 ± 0.07 in the control group (p = 0.865). The mean FD in the left interdental region is 1.37 ± 0.06 in the cases and 1.38 ± 0.05 in the control group (p = 0.369). The most common MCI score was C1, with 62% in the cases and 83.1% in the control group. MCI scores showed a statistically significant difference between cases and controls (p = 0.016, p < 0.05). While the C2 score was higher in the cases, the C1 score was higher in the controls. CONCLUSIONS: LT4 use was not associated with the FD of mandibular trabecular bone, but was associated with MCI values of cortical bone. Further studies on larger samples with different imaging modalities and image processing methods are needed.

Detection of pulpal calcifications on bite-wing radiographs using deep learning.

OBJECTIVES: Pulpal calcifications are discrete hard calcified masses of varying sizes in the dental pulp cavity. This study is aimed at measuring the performance of the YOLOv4 deep learning algorithm to automatically determine whether there is calcification in the pulp chambers in bite-wing radiographs. MATERIALS AND METHODS: In this study, 2000 bite-wing radiographs were collected from the faculty database. The oral radiologists labeled the pulp chambers on the radiographs as "Present" and "Absent" according to whether there was calcification. The data were randomly divided into 80% training, 10% validation, and 10% testing. The weight file for pulpal calcification was obtained by training the YOLOv4 algorithm with the transfer learning method. Using the weights obtained, pulp chambers and calcifications were automatically detected on the test radiographs that the algorithm had never seen. Two oral radiologists evaluated the test results, and performance criteria were calculated. RESULTS: The results obtained on the test data were evaluated in two stages: detection of pulp chambers and detection of pulpal calcification. The detection performance of pulp chambers was as follows: recall 86.98%, precision 98.94%, F1-score 91.60%, and accuracy 86.18%. Pulpal calcification "Absent" and "Present" detection performance was as follows: recall 86.39%, precision 85.23%, specificity 97.94%, F1-score 85.49%, and accuracy 96.54%. CONCLUSION: The YOLOv4 algorithm trained with bite-wing radiographs detected pulp chambers and calcification with high success rates. CLINICAL RELEVANCE: Automatic detection of pulpal calcifications with deep learning will be used in clinical practice as a decision support system with high accuracy rates in diagnosing dentists.

Evaluation of trabecular bone in individuals with periodontitis using fractal analysis: An observational cross-sectional study.

Background: It is very difficult to objectively evaluate the negative changes in bone structure due to periodontitis. The present study was aimed to evaluate the trabecular bone structure between healthy individuals and periodontitis patients by fractal analysis (FA) on digital panoramic radiographs. Material and Methods: The study included 50 periodontally healthy individuals (control group), 50 individuals with Stage 1 periodontitis (S1-P group), 50 individuals with Stage 2 periodontitis (S2-P), and 50 individuals with Stage 3 periodontitis (S3-P), a total of 200 individuals were included. The fractal dimension (FD) value of the trabecular bone in the interdental space between mandibular first molar and second premolar tooth roots was evaluated using Image J program. The mean FD values of the two regions were calculated by box counting method. Results: There was a statistically significant difference between the groups in terms of all periodontal parameter values (p<0.05). The mean FD values of individuals diagnosed with periodontitis were 1.36±0.08 in the S1-P group, 1.35±0.07 in the S2-P group, 1.28±0.15 in the S3-P group, and 1.44±0.06 in the control group. When the FD values between the groups were examined, it was seen that there was a statistically significant difference between the control and individuals with periodontitis, and the mean FD values were significantly higher in the healthy group (p<0.05). The best receiver operator curve was identified for periodontitis at the ≤1.409 cut-off FD value (area under the curve: 0.828; 95% CI: 0.758-0.899); p=0.000, p<0.001). Conclusions: FD evaluation can give an objective result about the effect of periodontitis on alveolar bone. The FD values of trabecular bone are different in healthy individuals and individuals with different stages of periodontitis. The findings suggested that a negative correlation between the periodontal data with the sites in which FD was measured and as the periodontitis stage progresses, FD decreases. Key words:Diagnosis, Periodontal Diseases, Radiographic Evaluation.

Comparison of five convolutional neural networks for predicting osteoporosis based on mandibular cortical index on panoramic radiographs.

OBJECTIVES: The aim of the present study was to compare five convolutional neural networks for predicting osteoporosis based on mandibular cortical index (MCI) on panoramic radiographs. METHODS: Panoramic radiographs of 744 female patients over 50 years of age were labeled as C1, C2, and C3 depending on the MCI. The data of the present study were reviewed in different categories including (C1, C2, C3), (C1, C2), (C1, C3), and (C1, (C2 +C3)) as two-class and three-class predictions. The data were separated randomly as 20% test data, and the remaining data were used for training and validation with fivefold cross-validation. AlexNET, GoogleNET, ResNET-50, SqueezeNET, and ShuffleNET deep-learning models were trained through the transfer learning method. The results were evaluated by performance criteria including accuracy, sensitivity, specificity, F1-score, AUC, and training duration. The Gradient-Weighted Class Activation Mapping (Grad-CAM) method was applied for visual interpretation of where deep-learning algorithms gather the feature from image regions. RESULTS: The dataset (C1, C2, C3) has an accuracy rate of 81.14% with AlexNET; the dataset (C1, C2) has an accuracy rate of 88.94% with GoogleNET; the dataset (C1, C3) has an accuracy rate of 98.56% with AlexNET; and the dataset (C1,(C2+C3)) has an accuracy rate of 92.79% with GoogleNET. CONCLUSION: The highest accuracy was obtained in the differentiation of C3 and C1 where osseous structure characteristics change significantly. Since the C2 score represent the intermediate stage (osteopenia), structural characteristics of the bone present behaviors closer to C1 and C3 scores. Therefore, the data set including the C2 score provided relatively lower accuracy results.

Evaluation of the mandibular trabecular bone in patients with bruxism using fractal analysis.

OBJECTIVE: The aims of this study were (1) to investigate the effect of bruxism on the fractal dimension (FD) of the mandibular trabecular bone through digital panoramic radiographs, and (2) to evaluate the effectiveness of fractal analysis as a diagnostic test for bruxism. METHODS: One hundred and six bruxer and 106 non-bruxer patients were included in the study. Three bilateral regions of interest (ROI) were selected: ROI-1, the mandibular condyle; ROI-2, the mandibular angle; ROI-3, the-area between the apical regions of the mandibular second premolar and the first molar teeth. FD values for the bruxer and non-bruxer groups were compared for each ROI. RESULTS: Only the FD measurements for the right mandibular condyle (ROI-1) showed a statistically significant difference (p = 0.041) between the bruxer and non-bruxer individuals. FD values measured in the bruxers (1.40 ± 0.09) were lower than in the non-bruxers (1.42 ± 0.08). CONCLUSION: Fractal analysis may be a useful method for discerning trabecular differences in the condylar areas of bruxer individuals. In future studies, the unilateral mastication habits, the characteristics of dental wear, and the occlusal bite forces of individuals should be documented.

Occlusal Caries Detection and Diagnosis Using Visual ICDAS Criteria, Laser Fluorescence Measurements, and Near-Infrared Light Transillumination Images.

OBJECTIVE: Current diagnostic tools for non-cavitated occlusal caries are not very reliable. For this reason, newer systems need to be developed. The aim of this study was to compare the performance of visual inspection (ICDAS-II), laser fluorescence (DIAGNOdent pen), and the near-infrared transillumination technique (DIAGNOcam) in the detection of non-cavitated occlusal caries lesions under clinical and laboratory conditions in 90 third molar teeth planned for extraction. MATERIALS AND METHODS: Ninety third molar teeth were firstly examined in clinical conditions, scored according to ICDAS-II criteria, and examined with DIAGNOdent pen and DIAGNOcam devices. After finishing the clinical examination, the teeth were re-evaluated shortly after the extractions with the same methods. Then, the teeth were sectioned for histological validation according to Downer's criteria. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic (ROC) curves were calculated based on the histological results. RESULTS: For the D0-D1-4 threshold, the area under the ROC curve values ranged between 0.754 and 0.881 for all systems. Sensitivity values ranged between 80.5 and 96.1%, and specificity values ranged between 61.5 and 84.6% for the three caries detection methods. DIAGNOcam had the best correlation value (0.616) according to histological observations and demonstrated a sensitivity rate of 96.1%, a specificity rate of 61.5%, and an accuracy rate of 91.1%. CONCLUSIONS: DIAGNOcam was found to be the most effective method for the diagnosis of occlusal caries without cavitation in permanent molar teeth.

What are the risk factors for maxillary sinus pathologies? A CBCT study.

OBJECTIVE: Some anatomic variations may interfere with proper airflow in the maxillary sinus and predispose to maxillary sinus pathologies. It was also reported that as a result of the transport of microorganisms from infected periapical tissues, maxillary sinus pathologies can develop. The objective of this study was to determine the potential relationships of maxillary sinus septa, concha bullosa, nasal septal deviation, and teeth with periapical lesion to maxillary sinus pathologies. METHODS: 200 cone beam computed tomography scans obtained at Necmettin Erbakan University, Faculty of Dentistry from 2013 to 2018 were retrospectively reviewed for the presence of maxillary sinus septa, concha bullosa, nasal septal deviation, teeth with periapical lesions, and maxillary sinus pathologies. When maxillary sinus mucosal thickening exceeded 2 mm, it was considered as a pathological condition. Logistic regression analysis was used to determine the risk factors for maxillary sinus pathologies. p < 0.05 considered statistically significant. RESULTS: 185 (46.2%) of the 400 maxillary sinuses showed maxillary sinus pathologies. Maxillary sinus septa, concha bullosa, and nasal septal deviation were not found to be as a risk factor for the maxillary sinus pathologies (p > 0.05). At least one apical lesion adjacent to the maxillary sinus increased the maxillary sinus pathology by 5.24 times on the right (OR 5.24, p < 0.001) and by 4.67 times on the left side (OR 4.67, p < 0.001). CONCLUSION: To prevent maxillary sinus pathologies, it is important for the teeth adjacent to the maxillary sinus to be healthy.

Is the maxillary sinus volume affected by concha bullosa, nasal septal deviation, and impacted teeth? A CBCT study.

PURPOSE: Various mechanisms play an important role in the growth of maxillary sinus cavities. The purpose of this study was to investigate the correlations of maxillary sinus volume (MSV) with nasal septal deviation (NSD), concha bullosa (CB) and impacted teeth using cone-beam computed tomography (CBCT) images. METHODS: From 55 patients, a total of 110 maxillary sinus images were obtained and examined. Data including age, gender, impacted third molar, canine teeth, NSD, and CB were examined. MSV was measured using the MIMICS software (Materialise HQ Technologielaan, Leuven, Belgium). All statistical analyses were performed using the SPSS (Statistical Package for Social Sciences, version 21) software and p values < 0.05 were considered to indicate statistical significance. RESULTS: Mean volume of the right maxillary sinus was 13.566 cm3, while the left was 13.882 cm3. The rate of patients with right and left impacted third molar teeth was 49.1% and 47.3%, respectively. The rate of right and left impacted canines was 1.8% and 5.5%, respectively. NSD was found in 56.4% of CBCT examinations and right and left CB were observed in 30.9% and 32.7% of the patients' examinations, respectively. Males had a significantly higher mean sinus volume than females for both sides (p < 0.05). There were no significant correlations between MSV and age (p > 0.05). No significant differences were found between MSV and impacted teeth, NSD and CB (p > 0.05). CONCLUSION: NSD, CB, impacted teeth, and age were not found to be related to MSV. Gender had an effect on MSV and males had higher mean sinus volume than females.

Diversion of the mandibular canal: Is it the best predictor of inferior alveolar nerve damage during mandibular third molar surgery on panoramic radiographs?

PURPOSE: The aim of this study was to evaluate the relationship between the mandibular canal and impacted mandibular third molars using cone-beam computed tomography (CBCT) and to compare the CBCT findings with signs on panoramic radiographs (PRs). MATERIALS AND METHODS: This retrospective study consisted of 200 mandibular third molars from 200 patients who showed a close relationship between the mandibular canal and impacted third molars on PRs and were referred for a CBCT examination of the position of the mandibular canal. The sample consisted of 124 females and 76 males, with ages ranging from 18 to 47 years (mean, 25.75±6.15 years). PRs were evaluated for interruption of the mandibular canal wall, darkening of the roots, diversion of the mandibular canal, and narrowing of the mandibular canal. Correlations between the PR and CBCT findings were statistically analyzed. RESULTS: In total, 146 cases (73%) showed an absence of canal cortication between the mandibular canal and impacted third molar on CBCT images. A statistically significant relationship was found between CBCT and PR findings (P<0.05). The absence of canal cortication on CBCT images was most frequently accompanied by the PR sign of diversion of the mandibular canal (96%) and least frequently by interruption of the mandibular canal wall (65%). CONCLUSION: CBCT examinations are highly recommended when diversion of the mandibular canal is observed on PR images to reduce the risk of mandibular nerve injury, and this sign appears to be more relevant than other PR signs.

Comparison of cone-beam computed tomography and panoramic radiography in the evaluation of maxillary sinus pathology related to maxillary posterior teeth: Do apical lesions increase the risk of maxillary sinus pathology?

PURPOSE: The aims of this study were first, to compare panoramic radiography with cone-beam computed tomography (CBCT) for evaluating topographic relationships, such as the classification of maxillary posterior teeth and their distance to the maxillary sinus floor; and second, to determine the relationship between maxillary sinus pathology and the presence of apical lesions. MATERIALS AND METHODS: In total, 285 paired CBCT and panoramic radiography records of patients (570 maxillary sinuses) were retrospectively analyzed. Both imaging modalities were used to determine the topographic relationship of the maxillary posterior teeth to the sinus floor. Mucosal thickening >2 mm was considered a pathological state. Data were analyzed using the chi-square, Wilcoxon, and Mann-Whitney U tests. Odds ratios (ORs) and confidence intervals (CIs) were calculated. RESULTS: The closest vertical distance measurements made between posterior maxillary teeth roots and the maxillary sinus on panoramic radiography and CBCT scans showed statistically significant differences from each other (P<0.05). Compared to panoramic radiography, CBCT showed higher mean values for the distance between the maxillary sinus floor and maxillary posterior teeth roots. The CBCT images showed that at least 1 apical lesion adjacent to the right maxillary sinus increased the risk of maxillary sinus pathology by 2.37 times (OR, 2.37; 95% CI, 1.58-3.55, P<0.05). CONCLUSION: Panoramic radiography might lead to unreliable diagnoses when evaluating the distance between the sinus floor and posterior roots of the maxillary teeth. Periapical lesions anatomically associated with maxillary sinuses were a risk factor for sinus mucosal thickening.

Comparison of cone-beam computed tomography with bitewing radiography for detection of periodontal bone loss and assessment of effects of different voxel resolutions: an in vitro study.

OBJECTIVE: The aim of the study was to compare intraoral radiographs and CBCT images for detection of horizontal periodontal bone loss, and to investigate the diagnostic effect of different voxel resolutions in CBCT imaging. METHODS: A total of 240 sites with horizontal bone loss were measured on the buccal, lingual, mesial, and distal surfaces of 60 posterior teeth in four maxillary and six mandibular bones obtained from cadavers (dry skulls). Direct measurements on the dry skulls were accepted as the gold standard values. Measurements on CBCT images at two different voxel resolutions (0.250 and 0.160 mm3) and intraoral bitewing radiographs were compared with one another and with the gold standard values. RESULTS: The measurements on the CBCT images at two voxel resolutions and bitewing radiographs did not differ significantly (p > 0.05) from the direct measurements on the dry skulls. No significant difference was found between the bitewing radiographs and CBCT images for measurements in the mesial and distal regions (p > 0.05). There was no significant difference between the measurements on the buccal and lingual surfaces at the two different voxel resolutions (p > 0.05). CONCLUSIONS: CBCT scans are recommended for evaluation of buccal and lingual bone loss to avoid intraoral radiographs that exceed routine examination of interproximal alveolar bone loss. Furthermore, instead of basing the voxel size on the required CBCT scans, it is recommended to select the smallest possible field of view to reduce the dose of radiation.

Is There a Possible Association between Skeletal Face Types and Third Molar Impaction? A Retrospective Radiographic Study.

OBJECTIVE: Third molar impaction is seen much more than impaction of any other tooth as they are the last teeth to erupt. Inadequate retromolar space and the direction of eruption may be contributing factors. The aim of this study was to investigate the relationship between third molar impaction and different skeletal face types. SUBJECTS AND METHODS: Panoramic and lateral cephalometric radiographs of 158 orthodontic patients (aged 19-25 years) were retrieved from the archived records of the Necmettin Erbakan University Faculty of Dentistry, Konya, Turkey. Third molar impaction was classified on the basis of Winter's classification. The skeletal facial type was determined by a measure of the angle created by the lines Ba-Na and Pt-Gn. The mean was 90 ± 2 and this value was regarded as mesofacial. An angle of > 93° was regarded as brachyfacial and an angle of < 87° as dolichofacial. RESULTS: The overall presence of mandibular and maxillary third molar impactions was 65.2 and 38.6%, respectively. Although there was a statistically significant difference between different skeletal facial types and mandibular third molar impaction (p < 0.05), no statistically significant differences were observed between different skeletal facial types and maxillary third molar impaction (p > 0.05). Brachyfacials demonstrated a lower prevalence of third molar impaction than dolichofacials. CONCLUSIONS: Different skeletal face types were associated with mandibular third molar impaction. Brachyfacials, who have a greater horizontal facial growth pattern than dolichofacials, showed a lower prevalence of impacted mandibular third molars.