Deep learning-based automatic segmentation of the mandibular canal on panoramic radiographs: A multi-device study.

Purpose: The objective of this study was to propose a deep-learning model for the detection of the mandibular canal on dental panoramic radiographs. Materials and Methods: A total of 2,100 panoramic radiographs (PANs) were collected from 3 different machines: RAYSCAN Alpha (n=700, PAN A), OP-100 (n=700, PAN B), and CS8100 (n=700, PAN C). Initially, an oral and maxillofacial radiologist coarsely annotated the mandibular canals. For deep learning analysis, convolutional neural networks (CNNs) utilizing U-Net architecture were employed for automated canal segmentation. Seven independent networks were trained using training sets representing all possible combinations of the 3 groups. These networks were then assessed using a hold-out test dataset. Results: Among the 7 networks evaluated, the network trained with all 3 available groups achieved an average precision of 90.6%, a recall of 87.4%, and a Dice similarity coefficient (DSC) of 88.9%. The 3 networks trained using each of the 3 possible 2-group combinations also demonstrated reliable performance for mandibular canal segmentation, as follows: 1) PAN A and B exhibited a mean DSC of 87.9%, 2) PAN A and C displayed a mean DSC of 87.8%, and 3) PAN B and C demonstrated a mean DSC of 88.4%. Conclusion: This multi-device study indicated that the examined CNN-based deep learning approach can achieve excellent canal segmentation performance, with a DSC exceeding 88%. Furthermore, the study highlighted the importance of considering the characteristics of panoramic radiographs when developing a robust deep-learning network, rather than depending solely on the size of the dataset.

Accuracy of CBCT in Preoperative Assessment of Impacted Canines vs. Panoramic Radiography.

Background: Accurate assessment of impacted canines is crucial for successful treatment planning. Radiographic techniques like Cone Beam Computed Tomography (CBCT) and Panoramic Radiography are commonly used, but their comparative accuracy remains under scrutiny. Materials and Methods: In this study, 50 patients diagnosed with impacted canines were enrolled. Both CBCT and Panoramic Radiography images were obtained for each patient using standard protocols. Two experienced dentists independently analyzed the images to determine the position of impacted canines and their relationship with neighboring structures. Results: The findings of this study revealed that CBCT provided superior accuracy in assessing the position of impacted canines compared to Panoramic Radiography. Specifically, CBCT demonstrated a mean accuracy rate of 89.5%, while Panoramic Radiography showed a mean accuracy rate of 72.3%. Moreover, CBCT allowed for better visualization of impacted canine angulation, depth, and spatial orientation. Panoramic Radiography, on the other hand, displayed limitations in precisely identifying the impacted canine's position. Conclusion: The study's outcomes underscore the higher accuracy of CBCT over Panoramic Radiography in the preoperative assessment of impacted canines. CBCT's detailed imaging provides valuable insights for treatment planning, potentially leading to improved surgical outcomes. Although CBCT entails greater radiation exposure and cost, its benefits in accurate diagnosis and treatment planning justify its use in cases of impacted canines.

Comparison of mandibular morphometric parameters in digital panoramic radiography in gender determination using machine learning.

OBJECTIVE: This study aimed to evaluate the usability of morphometric features obtained from mandibular panoramic radiographs in gender determination using machine learning algorithms. MATERIALS AND METHODS: High-resolution radiographs of 200 patients aged 20-77 (41.0 ± 12.7) were included in the study. Twelve different morphometric measurements were extracted from each digital panoramic radiography included in the study. These measurements were used as features in the machine learning phase in which six different machine learning algorithms were used (k-nearest neighbor, decision trees, support vector machines, naive Bayes, linear discrimination analysis, and neural networks). To evaluate the reliability, we have performed tenfold cross-validation and we repeated this 10 times for every classification process. This process enhances the reliability of the results for other datasets. RESULTS: When all 12 features are used together, the accuracy rate is found to be 82.6 ± 0.5%. The classification accuracies are also compared using each feature alone. Three features that give the highest accuracy are coronoid height (80.9 ± 0.9%), condyle height (78.2 ± 0.5%), and ramus height (77.2 ± 0.4%), respectively. When compared to the classification algorithms, the highest accuracy was obtained with the naive Bayes algorithm with a rate of 84.0 ± 0.4%. CONCLUSION: Machine learning techniques can accurately determine gender by analyzing mandibular morphometric structures from digital panoramic radiographs. The most precise results are achieved by evaluating the structures in combination, using attributes obtained from applying the MRMR algorithm to all features.

Detection and classification of mandibular fractures in panoramic radiography using artificial intelligence.

PURPOSE: This study aimed to assess the performance of a deep learning algorithm (YOLOv5) in detecting different mandibular fracture types in panoramic images. METHODS: This study utilized a dataset of panoramic radiographic images with mandibular fractures. The dataset was divided into training, validation, and testing sets, with 60%, 20%, and 20% of the images, respectively. An equal number of control panoramic radiographs, which did not contain any fractures, were also randomly distributed among the three sets. The YOLOv5 deep learning model was trained to detect six fracture types in the mandible based on the anatomical location including symphysis, body, angle, ramus, condylar neck, and condylar head. Performance metrics of accuracy, precision, sensitivity (recall), specificity, dice coefficient (F1 score), and area under the curve (AUC) were calculated for each class. RESULTS: A total of 498 panoramic images containing 673 fractures were collected. The accuracy was highest in detecting body (96.21%) and symphysis (95.87%), and was lowest in angle (90.51%) fractures. The highest and lowest precision values were observed in detecting symphysis (95.45%) and condylar head (63.16%) fractures, respectively. The sensitivity was highest in the body (96.67%) fractures and was lowest in the condylar head (80.00%) and condylar neck (81.25%) fractures. The highest specificity was noted in symphysis (98.96%), body (96.08%), and ramus (96.04%) fractures, respectively. The dice coefficient and AUC were highest in detecting body fractures (0.921 and 0.942, respectively), and were lowest in detecting condylar head fractures (0.706 and .812, respectively). CONCLUSION: The trained algorithm achieved promising performance metrics for the automated detection of most fracture types, with the highest performance observed in detecting body and symphysis fractures. Machine learning can provide a potential tool for assisting clinicians in mandibular fracture diagnosis.

Identification and classification of pulp calcifications in posterior teeth according to dental condition using digital panoramic radiography and cone beam computed tomography.

OBJECTIVE: To compare digital panoramic radiography (DPR) and cone beam computed tomography (CBCT) in the detection and classification of pulp calcifications in posterior teeth in relation to tooth condition and its location. METHODS: 250 patients with simultaneous DPR and CBCT scans were selected from a dental image bank. For each posterior tooth, its condition was registered (healthy, restored, or decayed). The presence of calcifications was assessed and classified according to location (coronal or radicular) and type (total obliteration, partial obliteration, narrowing, and no calcification). Chi-square and McNemar tests were used to compare DPR and CBCT results, with a significance level of 5%. DPR diagnostic values were calculated using CBCT as reference. RESULTS: Decayed and restored teeth showed a significantly (p < 0.001) higher prevalence of pulp calcifications than healthy teeth in both imaging exams. There was a significant discrepancy between the imaging modalities in the identification of calcifications (p < 0.001), especially for radicular calcifications of maxillary teeth (DPR = 55.2%; CBCT = 30.9%). In the case of coronal calcifications, there was a greater discrepancy between CBCT and DPR in the mandibular teeth (10.7%) than in the maxillary teeth (7.7%). Overall, DPR presents higher sensitivity (0.855) than specificity (0.483) for pulp calcifications detection. CONCLUSION: DPR considerably overestimates pulp calcifications in posterior teeth, as compared to CBCT. A higher prevalence of pulp calcifications is related to decayed and restored teeth.

Quantitative analysis of mandibular cortical morphology using artificial intelligence-based computer assisted diagnosis for panoramic radiography on underlying diseases and dental status in women over 20 years of age.

Background/purpose: Recently, an artificial intelligence-based computer-assisted diagnosis (AI-CAD) for panoramic radiography was developed to scan the inferior margin of the mandible and automatically evaluate mandibular cortical morphology. The aim of this study was to analyze quantitatively the mandibular cortical morphology using the AI-CAD, especially focusing on underlying diseases and dental status in women over 20 years of age. Materials and methods: 419 patients in women over 20 years of age who underwent panoramic radiography were included in this study. The mandibular cortical morphology was analyzed with an AI-CAD that evaluated the degree of deformation of the mandibular inferior cortex (MIC) and mandibular cortical index (MCI) automatically. Those were analyzed in relation to underlying diseases, such as diabetes, hypertension, dyslipidemia, rheumatism and osteoporosis, and dental status, such as the number of teeth present in the maxilla and mandible. Results: The degree of deformation of MIC in women under 51 years of age (21-50 years; n = 229, 16.0 ± 12.7) was significantly lower than those of over 50 years of age (51-90 years; n = 190, 45.1 ± 23.0), and the MCI was a significant difference for the different age group. Regarding the degree of deformation of MIC and MCI in women over 50 years of age, osteoporosis and number of total teeth present in the maxilla and mandible were significant differences. Conclusion: The results of this study indicated that the mandibular cortical morphology using the AI-CAD is significantly related to osteoporosis and dental status in women over 50 years of age.

DMAF-Net: Deformable multi-scale adaptive fusion network for dental structure detection with panoramic radiographs.

OBJECTIVES: Panoramic radiography is one of the most commonly used diagnostic modalities in dentistry. Automatic recognition of panoramic radiography helps dentists in decision support. In order to improve the accuracy of the detection of dental structural problems in panoramic radiographs, we have improved the YOLO network and verified the feasibility of this new method in aiding the detection of dental problems. METHODS: We propose a Deformable Multi-scale Adaptive Fusion Net (DMAF-Net) to detect five types of dental situations (impacted teeth, missing teeth, implants, crown restorations and root canal-treated teeth) in panoramic radiography by improving the You Only Look Once (YOLO) network. In DMAF-Net, we propose different modules to enhance the feature extraction capability of the network as well as to acquire high-level features at different scales, while using adaptive spatial feature fusion to solve the problem of scale mismatches of different feature layers, which effectively improves the detection performance. In order to evaluate the detection performance of the models, we compare the experimental results of different models in the test set, and select the optimal results of the models by calculating the average of different metrics in each category as the evaluation criteria. RESULTS: 1474 panoramic radiographs were divided into training, validation and test sets in the ratio of 7:2:1. In the test set, the average precision and recall of DMAF-Net are 92.7% and 87.6%, respectively; the mean Average Precision (mAP0.5 and mAP [0.5:0.95]) are 91.8% and 63.7%, respectively. CONCLUSIONS: The proposed DMAF-Net model improves existing deep learning models and achieves automatic detection of tooth structure problems in panoramic radiographs. This new method has great potential for new computer-aided diagnostic, teaching and clinical applications in the future.

A comparison of estimated age based on pulp volume from cone beam computed tomography (CT) images and panoramic radiography data with chronological age.

This retrospective study was conducted to evaluate different methods for dental age estimation in children and to examine the feasibility of using cone beam computed tomography (CBCT) data for age estimation. A total of 200 radiographic records (both digital panoramic radiographs and CBCTs) were acquired from 100 children aged 9 to 16 years, all taken on the same dates. Radiographic data was acquired from archived records and included both panoramic radiography and CBCT data belonging to the same individual. CBCT was used when panoramic radiographic data was insufficient. The pulp volume and pulp/tooth volume ratio of the left first molar teeth in the mandible were calculated from the CBCT data using MIMICS software. In addition, age was estimated by the Demirjian and Willems methods from data obtained from panoramic radiography images. Statistical analyses and linear regression analysis were performed as necessary. There was a statistically significant difference between the mean difference between the Demirjian method and chronological age, and between the Willems method and chronological age (p < 0.001). Statistically significance was achieved in a linear regression model created from pulp volume (R2 = 0.098) and pulp/tooth volume ratio (R2 = 0.395) data for the estimated dental age analysis (p < 0.001) and a negative correlation was observed with chronological age. When compared estimated dental age from CBCT data with chronological age, the pulp/tooth volume ratio method yielded results closer to chronological age than using only pulp volume data. When considering both panoramic radiographic age estimation methods and age estimation methods using CBCT data, we found that the results obtained with the Willems method, a panoramic radiographic age estimation technique, provided the closest results to the chronological age. More contributions should be made to the literature regarding the feasibility of age estimation using pulp and tooth volume as an alternative method.

Investigation of the relationship between the mandibular third molar teeth and the inferior alveolar nerve using posteroanterior radiographs: a pilot study.

BACKGROUND: The most severe complication that can occur after mandibular third molar (MM3) surgery is inferior alveolar nerve (IAN) damage. It is crucial to have a comprehensive radiographic evaluation to reduce the possibility of nerve damage. The objective of this study is to assess the diagnostic accuracy of panoramic radiographs (PR) and posteroanterior (PA) radiographs in identifying the association between impacted MM3 roots and IAN. METHODS: This study included individuals who had PR, PA radiographs, and cone beam computed tomography (CBCT) and who had at least one impacted MM3. A total of 141 impacted MM3s were evaluated on CBCT images, and the findings were considered gold standard. The relationship between impacted MM3 roots and IAN was also evaluated on PR and PA radiographies. The data was analyzed using the McNemar and Chi-squared tests. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of PR and PA radiographies were determined. RESULTS: Considering CBCT the gold standard, the relationship between MM3 roots and IAN was found to be statistically significant between PR and CBCT (p = 0.00). However, there was no statistically significant relationship between PA radiography and CBCT (0.227). The study revealed that the most prevalent limitation of the PR in assessing the relationship between MM3 roots and IAN was the identification of false-positive relationship. CONCLUSIONS: PA radiography may be a good alternative in developing countries to find out if there is a contact between MM3 roots and IAN because it is easier to get to, cheaper, and uses less radiation.

Performance of a commercially available Generative Pre-trained Transformer (GPT) in describing radiolucent lesions in panoramic radiographs and establishing differential diagnoses.

OBJECTIVES: To evaluate the performance of a commercially available Generative Pre-trained Transformer (GPT) in describing and establishing differential diagnoses for radiolucent lesions in panoramic radiographs. MATERIALS AND METHODS: Twenty-eight panoramic radiographs, each containing a single radiolucent lesion, were evaluated in consensus by three examiners and a commercially available ChatGPT-3.5 model. They provided descriptions regarding internal structure (radiodensity, loculation), periphery (margin type, cortication), shape, location (bone, side, region, teeth/structures), and effects on adjacent structures (effect, adjacent structure). Diagnostic impressions related to origin, behavior, and nature were also provided. The GPT program was additionally prompted to provide differential diagnoses. Keywords used by the GPT program were compared to those used by the examiners and scored as 0 (incorrect), 0.5 (partially correct), or 1 (correct). Mean score values and standard deviation were calculated for each description. Performance in establishing differential diagnoses was assessed using Rank-1, -2, and - 3. RESULTS: Descriptions of margination, affected bone, and origin received the highest scores: 0.93, 0.93, and 0.87, respectively. Shape, region, teeth/structures, effect, affected region, and nature received considerably lower scores ranging from 0.22 to 0.50. Rank-1, -2, and - 3 demonstrated accuracy in 25%, 57.14%, and 67.85% of cases, respectively. CONCLUSION: The performance of the GPT program in describing and providing differential diagnoses for radiolucent lesions in panoramic radiographs is variable and at this stage limited in its use for clinical application. CLINICAL RELEVANCE: Understanding the potential role of GPT systems as an auxiliary tool in image interpretation is imperative to validate their clinical applicability.

Bone pattern changes in post liver transplant patients using bisphosphonates.

OBJECTIVE: To identify radiographic findings suggestive of drug-induced osteonecrosis and evaluate radiomorphometric patterns indicative of changes in bone mineral density in individuals transplanted for liver disorders using bisphosphonates. STUDY DESIGN: The study group included panoramic x-rays of liver transplant patients who are being monitored and who present a clinical status of osteoporosis and use bisphosphonates. The control group was made up of liver transplant patients who did not have osteoporosis. On panoramic radiographs, mental index (MI) and mandibular cortical index (MCI) and the presence of radiographic anomalies suggestive of osteonecrosis were evaluated. RESULTS: There were significant statistical results when comparing the groups in relation to the decrease in bone mineral density (BMD) with MCI-C3 (p = 0.036), however, there were none in relation to MI (p = 0.14). There were no valid statistical results when relating MCI (p = 0.94) and MI (p = 0.66) with reduced BMD and use of bisphosphonates. CONCLUSION: Liver transplant individuals using bisphosphonates present greater radiographic signs of bone sclerosis suggestive of a greater propensity to develop osteonecrosis of the jaw and an increased risk of presenting changes suggestive of reduced bone mineral density on panoramic radiographs when compared to liver transplant individuals not using bisphosphonates.

Optimizing the use of panoramic radiography for determining the position of impacted permanent maxillary canines.

The purpose of this research was to test the reliability of a modified magnification method for determining the position of an impacted canine from a single panoramic radiograph. This retrospective study evaluated 114 panoramic radiographs showing 136 impacted maxillary canines. The widths of the impacted canines, contralateral erupted canines, and ipsilateral maxillary incisors were measured, and ratios for the canine-incisor index (CII) and canine-canine index (CCI) were calculated. The impacted canines were also classified according to their location in the vertical plane (apical, middle, or coronal zone) relative to the contralateral central incisor. Continuous data were analyzed for normal distribution, and logistic and multivariate logistic regression models were conducted. The Benjamini-Hochberg procedure with a false discovery rate of 0.05 was used to account for multiple testing. The intrarater reliability was excellent for impacted canine, central incisor, and contralateral canine measurements (intraclass correlation coefficient > 0.9). The CII and vertical zone were strong predictors of an impacted canine position with clinically useful sensitivity and specificity values (0.69 and 0.74, respectively, based on an area under the curve concordance statistic of 0.75). A predictive range was evident for the CII of palatally (1.10-1.39) and buccally (0.90-1.19) impacted canines in the middle and coronal zones, respectively. The occurrence of palatal or buccal positioning was not significantly associated with the CCI (P = 0.2). The CII and vertical zone identified from a single panoramic radiograph can be used to determine the buccopalatal position of an impacted canine, with more reliability if the impacted canine crown is in the middle or coronal zone of the contralateral central incisor.

A Multi-center Dental Panoramic Radiography Image Dataset for Impacted Teeth, Periodontitis, and Dental Caries: Benchmarking Segmentation and Classification Tasks.

Panoramic radiography imaging plays a crucial role in the diagnostic process of dental diseases. However, current artificial intelligence research datasets for panoramic radiography dental image processing are often limited to single-center and single-task scenarios, making it difficult to generalize their results. To address this, we present a multi-center, multi-task labeled dataset. In this study, our dataset comprises three datasets obtained from different hospitals. The first set has 4940 panoramic radiography images and corresponding labels from the Stemmatological Hospital of the General Hospital of Ningxia Medical University. The second set includes 716 panoramic radiography images and labels from the People's Hospital of Yinchuan City, Ningxia. The third dataset contains 880 panoramic radiography images and labels from a hospital in Shenzhen, Guangdong Province. This comprehensive dataset encompasses three types of dental diseases: impacted teeth, periodontitis, and dental caries. Specifically, it comprises 2555 images related to impacted teeth, 2735 images related to periodontitis, and 1246 images related to dental caries. In order to evaluate the performance of the dataset, we conducted benchmark tests for segmentation and classification tasks on our dataset. The results show that the presented dataset could be effectively used for benchmarking segmentation and classification tasks critical to the diagnosis of dental diseases. To request our multi-center dataset, please visit the address: https://github.com/qinxin99/qinxini .

Evaluation of Spectral X-Ray Imaging for Panoramic Dental Images Based on a Simulation Framework.

Modern photon counting detectors allow the calculation of virtual monoenergetic or material decomposed X-ray images but are not yet used for dental panoramic radiography systems. To assess the diagnostic potential and image quality of photon counting detectors in dental panoramic radiography, ethics approval from the local ethics committee was obtained for this retrospective study. Conventional CT scans of the head and neck region were segmented into bone and soft tissue. The resulting datasets were used to calculate panoramic equivalent thickness bone and soft tissue images by forward projection, using a geometry like that of conventional panoramic radiographic systems. The panoramic equivalent thickness images were utilized to generate synthetic conventional panoramic radiographs and panoramic virtual monoenergetic radiographs at various energies. The conventional, two virtual monoenergetic images at 40 keV and 60 keV, and material-separated bone and soft tissue panoramic equivalent thickness X-ray images simulated from 17 head CTs were evaluated in a reader study involving three experienced radiologists regarding their diagnostic value and image quality. Compared to conventional panoramic radiographs, the material-separated bone panoramic equivalent thickness image exhibits a higher image quality and diagnostic value in assessing the bone structure p < . 001 and details such as teeth or root canals p < . 001 . Panoramic virtual monoenergetic radiographs do not show a significant advantage over conventional panoramic radiographs. The conducted reader study shows the potential of spectral X-ray imaging for dental panoramic imaging to improve the diagnostic value and image quality.

Performance evaluation of three versions of a convolutional neural network for object detection and segmentation using a multiclass and reduced panoramic radiograph dataset.

OBJECTIVES: To evaluate the diagnostic performance of three versions of a deep-learning convolutional neural network in terms of object detection and segmentation using a multiclass panoramic radiograph dataset. METHODS: A total of 600 orthopantomographies were randomly selected for this study and manually annotated by a single operator using an image annotation tool (COCO Annotator v.11.0.1) to establish ground truth. The annotation classes included teeth, maxilla, mandible, inferior alveolar nerve, dento- and implant-supported crowns/pontics, endodontic treatment, resin-based restorations, metallic restorations, and implants. The dataset was then divided into training, validation, and testing subsets, which were used to train versions 5, 7, and 8 of You Only Look Once (YOLO) Neural Network. Results were stored, and a posterior performance analysis was carried out by calculating the precision (P), recall (R), F1 Score, Intersection over Union (IoU), and mean average precision (mAP) at 0.5 and 0.5-0.95 thresholds. The confusion matrix and recall precision graphs were also sketched. RESULTS: YOLOv5s showed an improvement in object detection results with an average R = 0.634, P = 0.781, mAP0.5 = 0.631, and mAP0.5-0.95 = 0.392. YOLOv7m achieved the best object detection results with average R = 0.793, P = 0.779, mAP0.5 = 0.740, and mAP0.5-0.95 = 0,481. For object segmentation, YOLOv8m obtained the best average results (R = 0.589, P = 0.755, mAP0.5 = 0.591, and mAP0.5-0.95 = 0.272). CONCLUSIONS: YOLOv7m was better suited for object detection, while YOLOv8m demonstrated superior performance in object segmentation. The most frequent error in object detection was related to background classification. Conversely, in object segmentation, there is a tendency to misclassify True Positives across different dental treatment categories. CLINICAL SIGNIFICANCE: General diagnostic and treatment decisions based on panoramic radiographs can be enhanced using new artificial intelligence-based tools. Nevertheless, the reliability of these neural networks should be subjected to training and validation to ensure their generalizability.

Comparison of periapical radiography, panoramic radiography, and CBCT in the evaluation of trabecular bone structure using fractal analysis.

OBJECTIVES: The aim of this study is to compare imaging techniques to evaluate trabecular bone structure using Fractal Analysis (FA). METHODS: Fifteen sheep hemimandibles were used for this study. Digital images were obtained using periapical radiography, panoramic radiography, and cone-beam computed tomography (CBCT). CBCT imaging was performed in standard (STD) and high-resolution (HR) modes. FA was conducted using ImageJ 1.3 software with the box-counting method on the images. The fractal dimension (FD) values were analyzed by the statistical software Jamovi 1.6.23. Statistical significance was accepted as p < 0.05. RESULTS: The highest mean FD value was the FD on digital periapical radiographs (PaFD) (1.28 ± 0.04), and the lowest mean FD value was the FD on standard resolution cone-beam computed tomography images (STD-CBCTFD) (1.12 ± 0.10). Although there was no statistically significant difference between the PaFD and the FD on digital panoramic radiographs (PanFD) (p = 0.485), the PaFD was found to be significantly higher than STD-CBCTFD (p < 0.001), and the FD on high-resolution cone-beam computed tomography images (HR-CBCTFD) (p = 0.007). The PanFD was found to be significantly higher than the STD-CBCTFD (p = 0.004). CONCLUSION: According to our results, in the evaluation of trabecular bone structure using FA, periapical radiographs and panoramic radiographs have similar image quality for assessment of the FD. On the other hand, CBCT results did not correlate with results from any of the other techniques in this study.

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.

Effect of using different strips on reducing the most common error in panoramic imaging: A randomised controlled trial on palatoglossal air space shadow.

INTRODUCTION: Panoramic radiography quality can be impaired by some errors such as positioning errors. Palatoglossal air space shadow error is one of the most common positioning errors and it is due to the tongue not sticking to the roof of the palate. Techniques used to deal with this error might help prevent unnecessary radiation to patients and save them time and money. The study aimed to investigate the effects of using celluloid matrix and edible tapes (fruit leather and chewing gum) on reducing the palatoglossal air space shadow error in panoramic imaging. METHODS: In our study, 270 patients referred to the Department of Radiology were randomised into three groups: a control group, a celluloid matrix group and an edible tapes group. Before panoramic imaging, all patients were instructed to adhere their tongues to the roof of their mouths, with the distinction that for the celluloid matrix and edible tapes groups, patients were asked to place celluloid tapes, fruit leathers, or chewing gums on their tongues before doing so. The routine imaging process was then performed, and the results were compared across groups to evaluate the incidence of palatoglossal air space shadow error. RESULTS: The number of error-free images in each fruit leather, chewing gum and celluloid tape group were significantly higher than the control group (all cases P < 0.05). The chances of error-free images in the fruit leather groups were the highest (9.57 times). The age (P = 0.136) and gender (P = 0.272) of patients had no significant effect on the results of interventions. CONCLUSION: The application of fruit leathers, chewing gums and celluloid tapes reduced the palatoglossal air space shadow error of panoramic imaging.

Prevalence and correlation with sex, age, and dental status of bone apposition at the mandibular angle and radiographic alterations of the temporomandibular joints: a retrospective observational study in an adult Swiss population.

BACKGROUND: The purpose of this study was to determine the prevalence of radiographic changes in the mandibular angle (bone apposition) and osseous alterations in the temporomandibular joints (TMJs) in the adult population of Switzerland. In addition, the study intended to investigate possible correlations between the two sites of contour bone changes (mandibular angle and TMJ) and to analyze various patient-related factors, including sex, age, dental status, and medical history. METHODS: Panoramic radiographs of 600 patients distributed into six age groups (283 females, 317 males, aged 20 to 79 years) were included to evaluate radiographic changes. The bone in the mandibular angle region and the shape of the condylar heads were examined for contour changes (bone apposition at the jaw angles and osseous changes of the TMJs). General estimating equations, binormal tests, and chi-squared tests were used for statistical analysis. RESULTS: Approximately half of the mandibular angles (47.8%) showed bone apposition, mostly bilateral. TMJ alterations were less common (27%), often unilateral, with flattening being the most frequent finding. No significant correlation was found between the two sites. Bone apposition at the mandibular angle showed a significant male predominance, whereas TMJ changes did not differ by sex. Alterations in both sites increased with age, and were not related to dental status or analgesic use. CONCLUSIONS: Bone apposition at the mandibular angle should be interpreted as part of the natural functional adaptation of the bone associated with aging. Assuming that parafunctional habits may influence the development and progression of alterations in the mandibular angle or TMJs, the presence of radiographic changes in these areas should prompt dental clinicians to investigate further in this direction. TRIAL REGISTRATION: The study was approved by the Swiss Association of Research Ethics Committees (swissethics), BASEC reference number: 2020-00963 (25.05.2020).

Mandibular Third Molar Impaction and Bone Change Distal to the Second Molar: A Panoramic Radiographic Study.

Background: The mandibular third molar is the most frequently impacted tooth. An impacted mandibular third molar (IMTM) can have negative consequences on the adjacent mandibular second molar (MSM), such as bone loss. An IMTM can be identified using orthopantomography (OPG). Our objective is to compare changes in bone level distal to the mandibular second molar (MSM) in patients with an extracted IMTM versus non-extracted IMTM using OPG. Methods: In this retrospective case-control study, 160 orthopantomograms (OPGs) of 80 patients who attended Dental Hospital of the University of Barcelona (HOUB) were randomly selected. Participants were stratified into a study group and control group. Results: Males and females experienced bone gain in the study group and bone loss in the control group. However, the difference in bone-level change was not statistically significant regarding gender in the study group. Within the study group, the age group of 29-39 years demonstrated significant (p-value = 0.042) bone gain after extraction compared to other age groups. However, the control group demonstrated bone loss in all age groups in which the difference is not statistically significant (p-value 0.794). Conclusions: Bone improvements distal to the MSM were observed after the extraction of an IMTM compared to when an IMTM was not extracted.