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In this study, we investigated whether deep learning-based prediction of immediate implant placement is possible. Panoramic radiographs of 201 patients with 874 teeth (Group 1: 440 teeth difficult to place implant immediately after extraction, Group 2: 434 teeth possible of immediate implant placement after extraction) for extraction were evaluated for the training and testing of a deep learning model. DenseNet121, ResNet18, ResNet101, ResNeXt101, InceptionNetV3, and InceptionResNetV2 were used. Each model was trained using preprocessed dental data, and the dataset was divided into training, validation, and test sets to evaluate model performance. For each model, the sensitivity, precision, accuracy, balanced accuracy, and F1-score were all greater than 0.90. The results of this study confirm that deep-learning-based prediction of the possibility of immediate implant placement is possible at a fairly accurate level., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Ethical approval and informed consent: This study was conducted in accordance with the guidelines of the World Medical Association Helsinki Declaration for Biomedical Research Involving Human Subjects and was approved by the public Institutional Review Board of South Korea (P01-202408-01-028). The public Institutional Review Board of South Korea waived the need for individual informed consent, either written or verbal, from the participants, owing to the non-interventional retrospective design of this study and because all data were analyzed anonymously., (© 2025. The Author(s).)

Background: The aim of this study was to evaluate the efficacy of detecting dental caries under fixed dental prostheses (FDPs) through the analysis of panoramic radiographs utilizing convolutional neural network (CNN) based You Only Look Once (YOLO) models. Deep learning algorithms can analyze datasets of dental images, such as panoramic radiographs to accurately identify and classify carious lesions. Using artificial intelligence, specifically deep learning methods, may help practitioners to detect and diagnose caries using radiograph images., Methods: The panoramic radiographs of 1004 patients, who had FDPs on their teeth and met the inclusion criteria, were divided into 904 (90%) images as training dataset and 100 (10%) images as the test dataset. Following the attainment of elevated detection scores with YOLOv7, regions of interest (ROIs) containing FDPs were automatically detected and cropped by the YOLOv7 model. In the second stage, 2467 cropped images were divided into 2248 (91%) images as the training dataset and 219 (9%) images as the test dataset. Caries under the FDPs were detected using both the YOLOv7 and the improved YOLOv7 (YOLOv7 + CBAM) models. The performance of the deep learning models used in the study was evaluated using recall, precision, F1, and mean average precision (mAP) scores., Results: In the first stage, the YOLOv7 model achieved 0.947 recall, 0.966 precision, 0.968 mAP and 0.956 F1 scores in detecting the FDPs. In the second stage the YOLOv7 model achieved 0.791 recall, 0.837 precision, 0.800 mAP and 0.813 F1 scores in detecting the caries under the FDPs, while the YOLOv7 + CBAM model achieved 0.827 recall, 0.834 precision, 0.846 mAP, and 0.830 F1 scores., Conclusion: The use of deep learning models to detect dental caries under FDPs by analyzing panoramic radiographs has shown promising results. The study highlights that panoramic radiographs with appropriate image features can be used in combination with a detection system supported by deep learning methods. In the long term, our study may allow for accurate and rapid diagnoses that significantly improve the preservation of teeth under FDPs., Competing Interests: Declarations. Ethical approval and consent to participate: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Non-Interventional Research Ethics Committee of the Kırıkkale University (Date: 28 September 2022, Decision No: 2022.09.22). There was no need for individual consent, and the need for informed consent was waived by the Non-Interventional Research Ethics Committee of the Kırıkkale University for this retrospective study because the data and patient details were anonymized. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. Clinical trial number: Not applicable., (© 2025. The Author(s).)

This study uses machine learning (ML) to elucidate the contact relationship between the mandibular third molar (M3M) and the inferior alveolar canal (IAC), leading to three major contributions; (1) The first publicly accessible PR image dataset with semantic annotations for 1,478 IACs and M3Ms from 1,010 patients is introduced, which includes challenging cases, such as false positive contacts, with CBCT images as the gold standard, (2) Established radiological indicators for M3M-IAC contact were extracted as features using digital image processing, and these features were used as inputs for various ML methods. Eligibility was assessed through statistical analysis and radiologists evaluations. Clinical feedback from radiologists on these features provides insights for future improvements. (3) ANNs, two custom CNNs, seven established DL models, and their combinations were used for automatic M3M-IAC contact determination with extracted features, semantic annotations, and ROIs. The ANN configuration surpassed both radiologists and DL models in specificity (82%), F1 score (92%), and accuracy (85%), while maintaining a comparable sensitivity (86%) to the DL models. This indicates that ANNs can effectively predict M3M-IAC contact relations and are particularly effective at identifying cases with no contact relation between M3M and IAC compared to other ML methods. Future work should focus on developing automated segmentation algorithms for M3M and IAC on PRs, to identify relevant anatomical structures, thereby improving clinical usability. The dataset, feature extraction, and ML codes are available through the CONTACT grand challenge., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Transfer learning (TL) is an alternative approach to the full training of deep learning (DL) models from scratch and can transfer knowledge gained from large-scale data to solve different problems. ImageNet, which is a publicly available large-scale dataset, is a commonly used dataset for TL-based image analysis; many studies have applied pre-trained models from ImageNet to clinical prediction tasks and have reported promising results. However, some have questioned the effectiveness of using ImageNet, which consists solely of natural images, for medical image analysis. The aim of this study was to evaluate whether pre-trained models using RadImageNet, which is a large-scale medical image dataset, could achieve superior performance in classification tasks in dental imaging modalities compared with ImageNet pre-trained models. To evaluate the classification performance of RadImageNet and ImageNet pre-trained models for TL, two dental imaging datasets were used. The tasks were (1) classifying the presence or absence of supernumerary teeth from a dataset of panoramic radiographs and (2) classifying sex from a dataset of lateral cephalometric radiographs. Performance was evaluated by comparing the area under the curve (AUC). On the panoramic radiograph dataset, the RadImageNet models gave average AUCs of 0.68 ± 0.15 (p < 0.01), and the ImageNet models had values of 0.74 ± 0.19. In contrast, on the lateral cephalometric dataset, the RadImageNet models demonstrated average AUCs of 0.76 ± 0.09, and the ImageNet models achieved values of 0.75 ± 0.17. The difference in performance between RadImageNet and ImageNet models in TL depends on the dental image dataset used., (© 2024. The Author(s).)

This study aims to evaluate the potential enhancement in implant classification performance achieved by incorporating artificially generated images of commercially available products into a deep learning process of dental implant classification using panoramic X-ray images. To supplement an existing dataset of 7,946 in vivo dental implant images, a three-dimensional scanner was employed to create implant surface models. Subsequently, implant surface models were used to generate two-dimensional X-ray images, which were compiled along with original images to create a comprehensive dataset. Images of 10 types of implants were classified using ResNet50 into the following datasets: (A) images of implants captured in vivo, (B) artificial implant images generated without background adjustments, and (C) implant images derived from in vivo images and generated with background adjustments. The classification accuracy was 0.8888 for dataset A, 0.903 for dataset B, and 0.9146 for dataset C. Notably, dataset C demonstrated the highest performance and exhibited the optimal feature distribution. In the context of deep learning classifiers for dental implants using panoramic X-ray images, incorporating artificially generated X-ray images-designed to mirror the appearance of human body implants-proved to be the most beneficial in enhancing the performance of the classification model., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

This study aimed to develop an artificial intelligence (AI) model for the screening of degenerative joint disease (DJD) using temporomandibular joint (TMJ) panoramic radiography and joint noise data. A total of 2631 TMJ panoramic images were collected, resulting in a final dataset of 3908 images (2127 normal (N) and 1781 DJD (D)) after excluding indeterminate cases and errors. AI models using GoogleNet were evaluated with six different combinations of image data, clinician-detected crepitus, and patient-reported joint noise. The model that integrated all joint noise data with imaging demonstrated the highest performance, achieving an F1-score of 0.72. Another model, which incorporated both imaging and crepitus, also achieved the same F1-score but had lower D recall (0.55 vs. 0.67) and N precision (0.71 vs. 0.74). The AI models outperformed orofacial pain specialists when provided with imaging alone or in combination with all joint noise data. These findings suggest that AI-enhanced DJD diagnosis using TMJ panoramic radiography and joint noise data offers a promising approach for early detection and improved patient care. The results underscore AI's capability to integrate diverse diagnostic factors, providing a comprehensive and accurate assessment that surpasses traditional methods., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

The current study aimed to evaluate the accuracy of Willems, Cameriere's and Greulich and Pyle method in age estimation among a sample of Egyptian children aged 8-16 years based on analysis of 140 panoramic dental X-ray and hand-wrist radiographs (70 girls and 70 boys). Using Willems method, the mean dental age underestimated chronological age by (0.20 ± 0.91 years) for boys and by (0.24 ± 1.33 year) for girls; these differences were not statistically significant. Also, Cameriere's method underestimated chronological age in both sexes by mean difference of (1.10 ± 1.22 year) in boys and (1.13 ± 1.31 year) in girls; these differences were statistically significant. Regarding Greulich and Pyle atlas, the mean of skeletal age was overestimated when compared to the real age in boys with a mean difference (0.04 ± 0.86 year). In girls; the age was underestimated by a mean difference of (0.15 ± 1.32 years) when compared to chronological age; these differences were not statistically significant. Comparing the two dental methods, the mean absolute error using Willems method was less than one year (0.94 and 0.96 years) in boys and girls respectively, while in Cameriere's method, it was more than one year in both groups. Moreover; the mean absolute error using Greulich and Pyle atlas was less than two years (1.02 and 1.38 years) in boys and girls respectively. Since Willems and the atlas methods were more accurate, the combination of both of them could be used for age estimation in the Egyptian population., Competing Interests: Competing interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Ethical approval: The study was approved by the Research Ethics Committee of Alexandria Faculty of Medicine, Alexandria University, Egypt (Approval number: 0201226, IRB number: 00012098, FWA number: 00018699). Informed consent: Written informed consent was obtained from each parent/ guardian of the studied children before participating in the study., (© 2025. The Author(s).)

Detection and teeth segmentation from X-rays, aiding healthcare professionals in accurately determining the shape and growth trends of teeth. However, small dataset sizes due to patient privacy, high noise, and blurred boundaries between periodontal tissue and teeth pose challenges to the models' transportability and generalizability, making them prone to overfitting. To address these issues, we propose a novel model, named Grouped Attention and Cross-Layer Fusion Network (GCNet). GCNet effectively handles numerous noise points and significant individual differences in the data, achieving stable and precise segmentation on small-scale datasets. The model comprises two core modules: Grouped Global Attention (GGA) modules and Cross-Layer Fusion (CLF) modules. The GGA modules capture and group texture and contour features, while the CLF modules combine these features with deep semantic information to improve prediction. Experimental results on the Children's Dental Panoramic Radiographs dataset show that our model outperformed existing models such as GT-U-Net and Teeth U-Net, with a Dice coefficient of 0.9338, sensitivity of 0.9426, and specificity of 0.9821. The GCNet model also demonstrates clearer segmentation boundaries compared to other models., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Ethical approval and informed consent: The data utilized in this study were obtained from publicly accessible databases that exclude any personally identifiable information. Hence, the question of informed consent does not apply. All procedures involving data handling adhered strictly to the ethical guidelines for research., (© 2025. The Author(s).)

Background/aim: This study aimed to investigate age- and sex-related morphological variations of the mandibular condyle, sigmoid notch, and coronoid process in a mid-sized German population using panoramic radiographs., Patients and Methods: A total of 150 participants (89 males, 61 females) aged between 18 and 80 years were included. Participants were divided by age and sex. Panoramic radiographs were obtained, and the right and left condylar processes, sigmoid notches, and coronoid processes were evaluated for shape variations., Results: Age-related differences were found in the mandibular condyles, with younger individuals exhibiting round-shaped condyles more frequently, while older participants showed a higher prevalence of flat-shaped condyles (p<0.001). Sex-related differences included females displaying more diamond-shaped condyles (p=0.033) and triangular-shaped coronoid processes (p=0.004), whereas males exhibited more round condylar processes., Conclusion: This study highlights significant age- and sex-related morphological variations in the mandibular condyles, sigmoid notches, and coronoid processes. These findings have important clinical implications for the diagnosis and treatment of temporomandibular joint disorders and maxillofacial surgery. Understanding these variations can improve diagnostic accuracy and surgical outcomes in patient-specific care., (Copyright © 2025, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Background: Artificial intelligence (AI) acts as the state-of-the-art in periodontitis diagnosis in dentistry. Current diagnostic challenges include errors due to a lack of experienced dentists, limited time for radiograph analysis, and mandatory reporting, impacting care quality, cost, and efficiency., Objective: This review aims to evaluate the current and future trends in AI for diagnosing periodontitis., Methods: A thorough literature review was conducted following PRISMA guidelines. We searched databases including PubMed, Scopus, Wiley Online Library, and ScienceDirect for studies published between January 2018 and December 2023. Keywords used in the search included "artificial intelligence," "panoramic radiograph," "periodontitis," "periodontal disease," and "diagnosis.", Results: The review included 12 studies from an initial 211 records. These studies used advanced models, particularly convolutional neural networks (CNNs), demonstrating accuracy rates for periodontal bone loss detection ranging from 0.76 to 0.98. Methodologies included deep learning hybrid methods, automated identification systems, and machine learning classifiers, enhancing diagnostic precision and efficiency., Conclusions: Integrating AI innovations in periodontitis diagnosis enhances diagnostic accuracy and efficiency, providing a robust alternative to conventional methods. These technologies offer quicker, less labor-intensive, and more precise alternatives to classical approaches. Future research should focus on improving AI model reliability and generalizability to ensure widespread clinical adoption.

Surgeons routinely interpret preoperative radiographic images for estimating the shape and position of the tooth prior to performing tooth extraction. In this study, we aimed to predict the difficulty of lower wisdom tooth extraction using only panoramic radiographs. Difficulty was evaluated using the modified Parant score. Two oral surgeons (a specialist and a clinical resident) predicted the difficulty level of the test data. This study also aimed to evaluate the performance of a deep learning model in predicting the necessity for tooth separation or bone removal during wisdom tooth extraction. Two convolutional neural networks (AlexNet and VGG-16) were created and trained using panoramic X-ray images. Both surgeons interpreted the same images and classified them into three groups. The accuracies for humans were 54.4% for both surgeons, 57.7% for AlexNet, and 54.4% for VGG-16. These results indicate that accurately predict the difficulty of wisdom teeth extraction using panoramic radiographs alone is challenging. However, AlexNet and VGG-16 had sensitivities of more than 90% for crown and root separation. The predictive ability of our proposed model is equivalent to that of an oral surgery specialist, and a recall value > 90% makes it suitable for screening in clinical settings., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Background: This retrospective study aimed to investigate the relationships between the radiographic features of impacted maxillary canines (IMCs) and traction duration and the factors affecting treatment success., Methods: Pre-treatment panoramic radiographs and patient records of 121 consecutive patients with IMCs were analyzed. The measurements included the angle of the IMC with the midline (α-angle), the horizontal position of the IMC relative to the adjacent teeth (S-Sector), the distance from the IMC to the occlusal plane (d-distance), the apex position of the IMC (A), and the vertical height of the IMC relative to the adjacent lateral tooth (V). The measurements were repeated 1 week later. The intraclass correlation coefficient was used to evaluate the relationship between two measurements. Binary logistic regression was performed to determine the factors affecting treatment success. Linear regression was conducted to determine the relationships between traction duration and other parameters. Receiver operating characteristic (ROC) curve analysis was performed to determine the α-angle and the d-distance cutoff values for treatment success. The significance level was set at p < 0.05., Results: The buccally IMCs were mostly located in sectors 1-2, whereas the palatally IMCs were mostly located in sectors 3-4-5. The effects of the parameters on the treatment success were significant according to the logistic regression of age (p = 0.003), d-distance (p = 0.002), and α-angle (p = 0.001). Linear regression analysis revealed that traction duration was statistically significant (p < 0.001)., Conclusion: According to the results of this retrospective study, the α-angle, d-distance, and sector can be used to predict the buccopalatal position of the IMC. The patient's age, d-distance, and α-angle affected the treatment success. The patient's age, d-distance, and the sector of the impacted canine affected the traction duration., Clinical Trial Number: Not applicable., Competing Interests: Declarations. Ethics approval and consent to participate: Approval for this retrospective study was obtained from the Ondokuz Mayıs University Faculty of Medicine Clinical Research Ethics Committee in Samsun, Turkey (date 03.24.2023 – approval number 2022/378). This retrospective cross-sectional study follows the principles of the Declaration of Helsinki. All patients (and parents of patients under 18 years of age) provided written informed consent that their data and radiological findings may be used for future research. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Background: Structured and standardized documentation is critical for accurately recording diagnostic findings, treatment plans, and patient progress in health care. Manual documentation can be labor-intensive and error-prone, especially under time constraints, prompting interest in the potential of artificial intelligence (AI) to automate and optimize these processes, particularly in medical documentation., Objective: This study aimed to assess the effectiveness of ChatGPT (OpenAI) in generating radiology reports from dental panoramic radiographs, comparing the performance of AI-generated reports with those manually created by dental students., Methods: A total of 100 dental students were tasked with analyzing panoramic radiographs and generating radiology reports manually or assisted by ChatGPT using a standardized prompt derived from a diagnostic checklist., Results: Reports generated by ChatGPT showed a high degree of textual similarity to reference reports; however, they often lacked critical diagnostic information typically included in reports authored by students. Despite this, the AI-generated reports were consistent in being error-free and matched the readability of student-generated reports., Conclusions: The findings from this study suggest that ChatGPT has considerable potential for generating radiology reports, although it currently faces challenges in accuracy and reliability. This underscores the need for further refinement in the AI's prompt design and the development of robust validation mechanisms to enhance its use in clinical settings., (©Daniel Stephan, Annika Bertsch, Matthias Burwinkel, Shankeeth Vinayahalingam, Bilal Al-Nawas, Peer W Kämmerer, Daniel GE Thiem. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 23.12.2024.)

The aim of this study was to analyse the effect of surgeons' experience and the benefit of using cone-beam computed tomography (CBCT) images, compared to the use of panoramic radiography (PAN) images, on their decisions with regard to mandibular third molar treatment modality. Panoramic radiographs and CBCTs from a total of 143 patients who had undergone impacted third molar surgery were randomly evaluated for treatment decision by 10 participants with differing clinical experience (5 novices and 5 experienced surgeons). The degree of agreement between the same type of participants was 'Substantial Agreement' (0.61-0.80) or 'Almost Perfect' (0.81-1.00). When the treatment modality decisions of the experienced and novice surgeons, using PAN and CBCT images, were compared, a statistically significant difference was found (p<0.01) between the variables. In 50 cases, experienced surgeons decided from CBCT images that total extraction was the best treatment method but opted for coronectomy when presented with PAN images of the same cases. In 164 cases novice surgeons decided on total extraction from CBCT images but chose coronectomy when presented with PAN images of the same cases. The results obtained from this study revealed that the degree of professional experience of the surgeon was a significant factor in determining the treatment decision. Moreover, the treatment choice of experienced surgeons was less affected by the change in imaging technique employed, when compared to novice surgeons., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 ADALI et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This study aimed to develop a deep learning system for the detection of three-rooted mandibular first molars (MFMs) on panoramic radiographs and to assess its diagnostic performance. Panoramic radiographs, together with cone beam computed tomographic (CBCT) images of the same subjects, were retrospectively collected from 730 patients, encompassing a total of 1444 MFMs (367 teeth were three-rooted and the remaining 1077 teeth were two-rooted). Five convolutional neural network (CNN) models (ResNet-101 and - 50, DenseNet-201, MobileNet-v3 and Inception-v3) were employed to classify three- and two-rooted MFMs on the panoramic radiographs. The diagnostic performance of each model was evaluated using standard metrics, including accuracy, sensitivity, specificity, precision, negative predictive value, and F1 score. Receiver operating characteristic (ROC) curve analyses were performed, with the CBCT examination taken as the gold standard.Among the five CNN models evaluated, ResNet-101 demonstrated superior diagnostic performance, and the AUC value attained was 0.907, significantly higher than that of all other models (all P < 0.01). The accuracy, sensitivity, and specificity were 87.5%, 83.6%, and 88.9%, respectively. DenseNet-201, however, showed the lowest diagnostic performance among the five models (all P < 0.01), with an AUC value of 0.701 and an accuracy of 73.2%. Overall, the performance of the CNNs diminished when using image patches containing only the distal half of MFMs, with AUC values ranging between 0.680 and 0.800. In contrast, the diagnostic performance of the two clinicians was poorer, with AUC values of only 0.680 and 0.632, respectively. In conclusion, the CNN-based deep learning system exhibited a high level of accuracy in the detection of three-rooted MFMs on panoramic radiographs., Competing Interests: Competing interests: The authors declare no competing interests. Ethical approval: Approval for this study was obtained from the Institutional Ethics Committee (IEC) for Human Research (Issuing Number: KY2022-089-01). Due to the retrospective nature of the study, the IEC of Suzhou Ninth People’s Hospital waived the need of obtaining informed consent. The study was performed in accordance with the Declaration of Helsinki., (© 2024. The Author(s).)

Objectives: Alveolar bone loss (ABL) and periodontal lesions are common diseases that have an undeniable effect on teeth maintenance and health. Current diagnostic methods include probing, intraoral radiography, and panoramic radiography; each has its limitations. In this study, we aimed to assess the diagnostic accuracy of panoramic radiography in the diagnosis of interdental ABL., Material and Methods: In this cross-sectional study, panoramic and cone-beam computed tomography (CBCT) images from 80 patients were collected from the archives of an oral and maxillofacial radiology center. The amount of ABL was obtained by measuring the distance from the Cemento-Enamel Junction (CEJ) to the alveolar bone crest on both panoramic and CBCT images. Patients were divided into healthy (ABL ≤ 2 mm) and diseased (2 mm < ABL) groups in terms of periodontal disease., Results: There was no statistically significant difference in the average ABL in the premolar, maxillary molar, and mandibular molar areas between the two techniques. However, in other areas, the ABL size was significantly lower in the panoramic view (p < 0.05). Also, the panoramic technique correctly recognized 89.1% of normal and 88.4% of ABL cases. The overall accuracy of panoramic radiography in the diagnosis of ABL was 85%, indicating the good accuracy of this technique. In maxilla, the highest diagnostic accuracy of the panoramic technique was in the molars, and the lowest was in the incisors. In the mandible, the highest and lowest diagnostic accuracy of the panoramic technique was related to molars and premolars, respectively. According to the kappa statistic, there was a significant good to very good agreement between the two types of techniques in all maxillary areas (p < 0.001)., Conclusions: Panoramic radiography is accurate in showing ABL. Measuring ABL in the posterior mandibular areas in panoramic radiographs is quite reliable; however, in general, digital panoramic radiography shows less ABL than the actual amount., (© 2024 The Author(s). Clinical and Experimental Dental Research published by John Wiley & Sons Ltd.)

Objectives: Panoramic radiographs (PRs) can reveal an incidental finding of atherosclerosis, or carotid artery calcification (CAC), in 3-15% of examined patients. However, limited training in identification of such calcifications among dental professionals results in missed diagnoses. This study aimed to detect CAC on PRs using an artificial intelligence (AI) model based on a vision transformer., Methods: 6,404 PRs were obtained from one hospital and screened for the presence of CAC based on electronic medical records. CAC was manually annotated with bounding boxes by an oral radiologist and reviewed and revised by three experienced clinicians to achieve consensus. An AI approach based on Faster R-CNN and Swin Transformer was trained and evaluated based on 185 PRs with CAC and 185 PRs without CAC. Reported and replicated diagnostic performances of published AI approaches based on convolutional neural networks (CNNs) were used for comparison. Quantitative evaluation of the performance of the models included precision, F1-score, recall, area-under-the-curve (AUC), and average precision (AP)., Results: The proposed method based on Faster R-CNN and Swin Transformer achieved a precision of 0.895, recall of 0.881, F1-score of 0.888, AUC of 0.950, and AP of 0.942, surpassing models based on a CNN., Conclusions: The detection performance of this newly developed and validated model was improved compared to previously reported models., Clinical Significance: Integrating AI models into dental imaging to assist dental professionals in the detection of CAC on PRs has the potential to significantly enhance the early detection of carotid artery atherosclerosis and its clinical management., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Objective: To assess and compare the validity of 2D modified Easy Box and measurement of the Beta angle on standard conventional orthopantomogram (OPG) versus 3D cone-beam computed tomography (CBCT) OPG-constructed view., Design: A retrospective agreement study., Methods: The aim of this study was to construct an Easy Box on a standard conventional OPG and to validate this novel method by comparing it with the Easy Box method on 3D CBCT. After approval from the Ethics Committee, OPG and CBCT radiographs were obtained for the study from departmental records and five private practices in the same location (Indore, India). The radiographs were selected based on record availability and with written consent from the participants before the commencement of the study. The records were analysed to enable a comparison and to assess the accuracy of Easy Box construction on both 3D CBCT and standard conventional OPG radiographs. The location of the impacted canine within the Easy Box boundaries and the measurement of the Beta angle were determined on both views., Results: A perfect agreement was obtained for the comparison of 3D Easy Box CBCT analysis with 2D modified Easy Box on OPG for impacted maxillary canines (Kappa = 1.0). A Bland-Altman (LoA) analysis showed no proportional bias in the comparison of the Beta angle on 3D and 2D OPG radiographs., Conclusion: Beta angle and 2D modified Easy Box on a conventional OPG yield similar results when compared to Easy Box on 3D CBCT OPG-constructed view. The standard OPG was valuable and cost-effective, particularly in the early stages of diagnosis and treatment planning, either as a substitute or when CBCT was unavailable., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose: Oral focus screening is recommended before HCT (haematopoietic cell transplantation). Acute foci are generally treated pre-HCT. However, it is unclear whether chronic foci should be treated pre-HCT. This study aimed to evaluate the association between number of foci and early post-HCT morbidity and mortality., Methods: Patients who received an oral screening pre-HCT at Radboudumc between 2013 and 2023, with a recent panoramic radiograph, were included. Foci were defined as teeth or implants with deep pockets, furcation radiolucencies, periapical radiolucencies, deep caries, partial eruption or retained roots. Dental interventions were provided pre-HCT if deemed necessary and possible. Hospital length of stay (LOS), mortality and oral exacerbations were assessed in the first hundred days post-HCT. Hospital LOS was log-transformed to normalize its skewed distribution. Multivariate regression analyses were performed., Results: Five hundred patients were included (median age 58 years, 37% female, 56% allogeneic). Four acute foci in two patients remained untreated pre-HCT. Forty-eight percent had at least one untreated chronic focus pre-HCT (mostly furcation radiolucencies and post-endodontic periapical lesions). Mean LOS was 22 days; 20 patients died, and four patients had an oral exacerbation before HCT day + 100. After adjustment for conditioning intensity, age, socioeconomic status and number of teeth, HCT recipients with ≥ 3 foci had five additional days in the hospital compared to those without foci (ratio of means 1.21; 95% CI 1.00 - 1.46). The number of foci was not associated with early post-HCT mortality., Conclusion: The presence of ≥ 3 oral foci is associated with extended hospital LOS in the first hundred days post-HCT., Competing Interests: Declarations. Ethics approval: Ethical approval was waived by the ethical committee of the Radboudumc because this research was not subjected to the law governing research involving human subjects. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

BACKGROUND This retrospective study of 44 patients requiring removable functional dental appliances aimed to compare the use of a maxillary expansion device, twinblock/monoblock, face mask, and chincup on maxillary sinus dimensions, mandibular anterior region bone density (MABD), and mandibular condyle head bone formation (MCBF) using panoramic radiography. MATERIAL AND METHODS The patients were divided into 4 groups based on the treatment modalities used: group A, maxillary expansion device (n=11); group B, twinblock/monoblock (n=11); group C, face mask (n=11); and group D, chincup (n=11). MABD and MCBF were assessed using the fractal analysis method, while maxillary sinus dimensions were compared using the ImageJ program. Different groups of patients were tested by the Mann-Whitney U test, with Bonferroni correction. RESULTS Group A showed an increase in MABD, MCBF, and average maxillary sinus indentation base length (MSIL) and a decrease in average planar base length (MSPL) and maxillary sinus area (MSA) (P<0.05). Group B showed an increase in MABD, MSIL, MSPL, MSA, and MCBF (P<0.05). Group C showed an increase in MABD, MSIL, MSPL, and MCBF, with a decrease in MSA (P>0.05). Group D showed an increase in MCBF, MABD, MSIL, and MSA, with a decrease in MSPL at the end of treatment, compared with pretreatment (P<0.05). CONCLUSIONS Orthodontic treatment with different removable functional dental appliances has varying effects on the mandible and maxilla. Possible changes in the maxilla and mandible should be considered before treatments with removable functional dental appliances.

Periapical periodontitis may manifest as a radiographic lesion radiographically. Periapical lesions are amongst the most common dental pathologies that present as periapical radiolucencies on panoramic radiographs. The objective of this research is to assess the diagnostic accuracy of an artificial intelligence (AI) model based on U²-Net architecture in the detection of periapical lesions on dental panoramic radiographs and to determine whether they can be useful in aiding clinicians with diagnosis of periapical lesions and improving their clinical workflow. 400 panoramic radiographs that included at least one periapical radiolucency were selected retrospectively. 780 periapical radiolucencies in these anonymized radiographs were manually labeled by two independent examiners. These radiographs were later used to train the AI model based on U²-Net architecture trained using a deep supervision algorithm. An AI model based on the U²-Net architecture was implemented. The model achieved a dice score of 0.8 on the validation set and precision, recall, and F1-score of 0.82, 0.77, and 0.8 respectively on the test set. This study has shown that an AI model based on U²-Net architecture can accurately diagnose periapical lesions on panoramic radiographs. The research provides evidence that AI-based models have promising applications as adjunct tools for dentists in diagnosing periapical radiolucencies and procedure planning. Further studies with larger data sets would be required to improve the diagnostic accuracy of AI-based detection models., (© 2024. The Author(s).)

Abstract: The presence of specific craniofacial deviations may be evidence of abnormal early brain development in bipolar disorder (BD). This study aimed to compare mandibular morphology in patients with BD versus nonpsychiatric controls using panoramic radiography. A total of 215 recorded images, 70 patients with BD and 145 controls, were evaluated. Seven mandibular parameters of the ramus height (RH), condylar height (CH), gonial angle (GA), antegonial angle (AGA), antegonial notch depth (AND), ramal notch depth (RND), and bigonial width (BGW) were measured in a blinded manner. Group analysis was conducted separately for males and females. The univariate comparisons showed increased values in RH, CH, AND, RND, and BGW and decreased values in GA in males with BD compared with male controls. In addition, females with BD had higher RH, CH, RND, and BGW values in comparison to female controls. The differences were statistically significant. Data from multivariate analyses revealed that increases in RH and BGW values in both sexes were significant predictive variables that distinguished patients with BD from controls. Moreover, deeper AND was the most prominent predictor that distinguished male patients with BD from male controls. The identification of subtle changes in mandibular measurements is consistent with the notion of impaired neurodevelopment in BD., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Tooth instance segmentation of dental panoramic X-ray images is of significant clinical importance. Teeth exhibit symmetry within the upper and lower jawbones and are arranged in a specific order. However, previous studies frequently overlook this crucial spatial prior information, resulting in the misidentifications of tooth categories, especially for adjacent or similarly shaped teeth. In this paper, we propose SPGTNet, a spatial prior-guided transformer method, designed to both the extracted tooth positional features from CNNs and the long-range contextual information from vision transformers, specifically for dental panoramic X-ray image segmentation. Initially, a center-based spatial prior perception module is employed to identify the centroid of each tooth, thereby enhancing the spatial prior information for the CNN sequence features. Subsequently, a bi-directional cross-attention module is designed to facilitate the interaction between the spatial prior information of the CNN sequence features and the long-range contextual features of the vision transformer sequence features. Finally, an instance identification head is employed to generate the tooth segmentation results. Extensive experiments on three public benchmark datasets demonstrate the effectiveness and superiority of our proposed method compared to other state-of-the-art approaches. The proposed method accurately identifies and analyzes tooth structures, thereby providing crucial information for dental diagnosis, treatment planning, and research.

The advent of precision diagnostics in pediatric dentistry is shifting towards ensuring early detection of dental diseases, a critical factor in safeguarding the oral health of the younger population. In this study, an innovative approach is introduced, wherein Discrete Wavelet Transform (DWT) and Generative Adversarial Networks (GANs) are synergized within an Image Data Fusion (IDF) framework to enhance the accuracy of dental disease diagnosis through dental diagnostic systems. Dental panoramic radiographs from pediatric patients were utilized to demonstrate how the integration of DWT and GANs can significantly improve the informativeness of dental images. In the IDF process, the original images, GAN-augmented images, and wavelet-transformed images are combined to create a comprehensive dataset. DWT was employed for the decomposition of images into frequency components to enhance the visibility of subtle pathological features. Simultaneously, GANs were used to augment the dataset with high-quality, synthetic radiographic images indistinguishable from real ones, to provide robust data training. These integrated images are then fed into an Artificial Neural Network (ANN) for the classification of dental diseases. The utilization of the ANN in this context demonstrates the system's robustness and culminates in achieving an unprecedented accuracy rate of 0.897, 0.905 precision, recall of 0.897, and specificity of 0.968. Additionally, this study explores the feasibility of embedding the diagnostic system into dental X-ray scanners by leveraging lightweight models and cloud-based solutions to minimize resource constraints. Such integration is posited to revolutionize dental care by providing real-time, accurate disease detection capabilities, which significantly reduces diagnostical delays and enhances treatment outcomes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The aim of this study was to propose a novel method to identify individuals by recognizing dentition change, along with human identification process using deep learning. Recent and past images of adults aged 20-49 years with more than two dental panoramic radiographs (DPRs) were assumed as postmortem (PM) and antemortem (AM) images, respectively. The dataset contained 1,029 paired PM-AM DPRs from 2000 to 2020. After constructing a database of AM dentition, the degree of similarity was calculated and sorted in descending order. The matched rank of AM identical to an unknown PM was measured by extracting candidate groups (CGs). The percentage of rank was calculated as the success rate, and similarity scores were compared based on imaging time intervals. The matched AM images were ranked in the CG with success rates of 83.2%, 72.1%, and 59.4% in the imaging time interval for extracting the top 20.0%, 10.0%, and 5.0%, respectively. The success rates depended on sex, and were higher for women than for men: the success rates for the extraction of the top 20.0%, 10.0%, and 5.0% were 97.2%, 81.1%, and 66.5%, respectively, for women and 71.3%, 64.0%, and 52.0%, respectively, for men. The similarity score differed significantly between groups based on the imaging time interval of 17.7 years. This study showed outstanding performance of convolutional neural network using dental panoramic radiographs in effectively reducing the size of AM CG in identifying humans., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Choi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Recently, deep learning has been increasingly applied in the field of dentistry. The aim of this study is to develop a model for the automatic segmentation, numbering, and state assessment of teeth on panoramic radiographs., Methods: We created a dual-labeled dataset on panoramic radiographs for training, incorporating both numbering and state labels. We then developed a fusion model that combines a YOLOv9-e instance segmentation model with an EfficientNetv2-l classification model. The instance segmentation model is used for tooth segmentation and numbering, whereas the classification model is used for state evaluation. The final prediction results integrate tooth position, numbering, and state information. The model's output includes result visualization and automatic report generation., Results: Precision, Recall, mAP50 (mean Average Precision), and mAP50-95 for the tooth instance segmentation task are 0.989, 0.955, 0.975, and 0.840, respectively. Precision, Recall, Specificity, and F1 Score for the tooth classification task are 0.943, 0.933, 0.985, and 0.936, respectively., Conclusions: This fusion model is the first to integrate automatic dental segmentation, numbering, and state assessment. It provides highly accurate results, including detailed visualizations and automated report generation., (© 2024. The Author(s).)

In the domain of medical imaging, the advent of deep learning has marked a significant progression, particularly in the nuanced area of periodontal disease diagnosis. This study specifically targets the prevalent issue of scarce labeled data in medical imaging. We introduce a novel unsupervised few-shot learning algorithm, meticulously crafted for classifying periodontal diseases using a limited collection of dental panoramic radiographs. Our method leverages UNet architecture for generating regions of interest (RoI) from radiographs, which are then processed through a Convolutional Variational Autoencoder (CVAE). This approach is pivotal in extracting critical latent features, subsequently clustered using an advanced algorithm. This clustering is key in our methodology, enabling the assignment of labels to images indicative of periodontal diseases, thus circumventing the challenges posed by limited datasets. Our validation process, involving a comparative analysis with traditional supervised learning and standard autoencoder-based clustering, demonstrates a marked improvement in both diagnostic accuracy and efficiency. For three real-world validation datasets, our UNet-CVAE architecture achieved up to average 14% higher accuracy compared to state-of-the-art supervised models including the vision transformer model when trained with 100 labeled images. This study not only highlights the capability of unsupervised learning in overcoming data limitations but also sets a new benchmark for diagnostic methodologies in medical AI, potentially transforming practices in data-constrained scenarios., (© 2024. The Author(s).)

Objective: The purpose of the present study was to verify the diagnostic performance of an AI system for the automatic detection of teeth, caries, implants, restorations, and fixed prosthesis on panoramic radiography., Methods: This is a cross-sectional study. A dataset comprising 1000 panoramic radiographs collected from 500 adult patients was analyzed by an AI system and compared with annotations provided by two oral and maxillofacial radiologists., Results: A strong correlation (R > 0.5) was observed between AI perception and observers 1 and 2 in carious teeth (0.691-0.878), implants (0.770-0.952), restored teeth (0.773-0.834), teeth with fixed prostheses (0.972-0.980), and missing teeth (0.956-0.988)., Discussion: Panoramic radiographs are commonly used for diagnosis and treatment planning. However, they often suffer from artifacts, distortions, and superimpositions, leading to potential misinterpretations. Thus, an automated detection system is required to tackle these challenges. Artificial intelligence (AI) has revolutionized various fields, including dentistry, by enabling the development of intelligent systems that can assist in complex tasks such as diagnosis and treatment planning., Conclusion: The automatic detection by the AI system was comparable to oral radiologists and may be useful for automatic identifications in panoramic radiographs. These findings signify the potential for AI systems to enhance diagnostic accuracy and efficiency in dental practices, potentially reducing the likelihood of diagnostic errors caused by unexperienced professionals., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Introduction: Accurate bracket positioning remains challenging. To avoid angulation errors, some recommend examining the panoramic radiograph during bonding. However, it can cause distortions. Cone-beam computed tomography (CBCT) provides a more precise panoramic reconstruction but with higher radiation doses. The main objective of this study is to compare the accuracy of axial positioning between direct bonding without radiography, with conventional panoramic radiograph, and with panoramic reconstruction from CBCT. The secondary objectives are to evaluate positioning accuracy of each tooth and to assess the influence of practitioner level of experience., Methods: Thirty practitioners, divided into two groups based on their experience performed direct bonding on a model thrice: without radiography, then with the conventional panoramic radiograph, then with the panoramic reconstruction from CBCT. Models were scanned, and angulation errors were measured using OrthoAnalyzer. Values were compared using the Friedman's test followed by the Bonferroni correction for multiple comparisons (P-value = 0.05)., Results: For the low level of experience group, angulation errors were significantly greater than the accepted limit without radiographic reference, and significantly lower with CBCT reconstruction. For the high level of experience group, angulation errors were significantly lower than the accepted limit for the three bonding methods. For every tooth, using the panoramic reconstruction from CBCT as a reference, was the most accurate method, regardless of the level of experience. More experienced practitioners made fewer errors for the three methods., Conclusions: Panoramic reconstruction from CBCT is the most accurate method to limit angulation errors during direct bonding. Conventional panoramic radiography remains a reliable tool if used with caution. Bonding without any radiographic reference should be avoided especially for less experienced practitioners., (Copyright © 2024. Published by Elsevier Inc.)

Introduction: The objective of this study was to compare the length ratios obtained on panoramic radiography and computed tomography (CT) to verify whether the former is adequate for diagnosing coronoid process hyperplasia., Methods: A case series of patients with coronoid process hyperplasia was investigated. Length ratios between the coronoid process and condyle were measured on panoramic radiographs by using the Levandoski method and on CT scans by using the methods described by Tavassol et al. and Stopa et al. The mean length ratios obtained using the three measurement methods were compared., Results: The mean length ratio measured with the Levandoski method was significantly lower than that measured with the method described by Stopa et al. (1.09 [0.09] vs. 1.21 [0.09]; P = 0.0001) and lower than that measured with the method described by Tavassol et al. (1.09 [0.09] vs. 1.34 [0.44]; P = 0.013)., Conclusion: Panoramic measurement of the coronoid process by using the Levandoski method tended to underestimate the length ratio, emphasizing the importance of using a scanographic measurement method at the slightest doubt to confirm the diagnosis of coronoid process hyperplasia., Competing Interests: Declaration of Competing Interest The authors have no financial or non-financial interests to disclose., (Copyright © 2023 Elsevier Masson SAS. All rights reserved.)

The use of artificial intelligence algorithms (AI) has gained importance for dental applications in recent years. Analyzing AI information from different sensor data such as images or panoramic radiographs (panoramic X-rays) can help to improve medical decisions and achieve early diagnosis of different dental pathologies. In particular, the use of deep learning (DL) techniques based on convolutional neural networks (CNNs) has obtained promising results in dental applications based on images, in which approaches based on classification, detection, and segmentation are being studied with growing interest. However, there are still several challenges to be tackled, such as the data quality and quantity, the variability among categories, and the analysis of the possible bias and variance associated with each dataset distribution. This study aims to compare the performance of three deep learning object detection models-Faster R-CNN, YOLO V2, and SSD-using different ResNet architectures (ResNet-18, ResNet-50, and ResNet-101) as feature extractors for detecting and classifying third molar angles in panoramic X-rays according to Winter's classification criterion. Each object detection architecture was trained, calibrated, validated, and tested with three different feature extraction CNNs which are ResNet-18, ResNet-50, and ResNet-101, which were the networks that best fit our dataset distribution. Based on such detection networks, we detect four different categories of angles in third molars using panoramic X-rays by using Winter's classification criterion. This criterion characterizes the third molar's position relative to the second molar's longitudinal axis. The detected categories for the third molars are distoangular, vertical, mesioangular, and horizontal. For training, we used a total of 644 panoramic X-rays. The results obtained in the testing dataset reached up to 99% mean average accuracy performance, demonstrating the YOLOV2 obtained higher effectiveness in solving the third molar angle detection problem. These results demonstrate that the use of CNNs for object detection in panoramic radiographs represents a promising solution in dental applications.

Orthopantomography (OPG) is a routine imaging method in dental practice and an essential di- agnostic tool in dentistry. However, OPGs are challenging to interpret due to many overlapping structures. Graduates of dental schools should be aware of image distortions caused by various factors and be able to distinguish them from typical structures to make an accurate diagnosis. The aim was to determine the correlation between the knowledge regarding the location of craniofacial structures of the 1st through 3rd- year dental students and the ability to recognized them on OPGs. The study was conducted in 2021 on 131 dental students using the Microsoft Teams program. Each participant had to determine the location of 4 anthropometric points on 4 OPGs. Using proprietary software, the authors determined the Articular angle between them. The researchers performed the statistical analysis. The Bioethics Committee approved the research. The results of students from particular years of studies did not show statistical significance. There was no statistically significant difference between males and females. Only the answers from third-year male students regarding the one Articular angle showed statistically significant differences compared to the rest of the participants. Recognizing joint structures on OPG is very important from the clinical point of view, although it is often overlooked in diagnostics. Students in the first years of dental school acquire the ability to assess and analyze TMJ on OPGs properly. OPGs have many advantages, including high availability, low cost and low radiation dose. In conclusion we can say that in the first three years of studies, dental students' image assessment skills in the craniofacial radiology field remain the same.

Purpose: This study aims to develop a deep learning framework for the automatic detection of the position relationship between the mandibular third molar (M3) and the mandibular canal (MC) on panoramic radiographs (PRs), to assist doctors in assessing and planning appropriate surgical interventions., Methods: Datasets D1 and D2 were obtained by collecting 253 PRs from a hospitals and 197 PRs from online platforms. The RPIFormer model proposed in this study was trained and validated on D1 to create a segmentation model. The CycleGAN model was trained and validated on both D1 and D2 to develop an image enhancement model. Ultimately, the segmentation and enhancement models were integrated with an object detection model to create a fully automated framework for M3 and MC detection in PRs. Experimental evaluation included calculating Dice coefficient, IoU, Recall, and Precision during the process., Results: The RPIFormer model proposed in this study achieved an average Dice coefficient of 92.56 % for segmenting M3 and MC, representing a 3.06 % improvement over the previous best study. The deep learning framework developed in this research enables automatic detection of M3 and MC in PRs without manual cropping, demonstrating superior detection accuracy and generalization capability., Conclusion: The framework developed in this study can be applied to PRs captured in different hospitals without the need for model fine-tuning. This feature is significant for aiding doctors in accurately assessing the spatial relationship between M3 and MC, thereby determining the optimal treatment plan to ensure patients' oral health and surgical safety., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

This paper addresses a relevant problem in Forensic Sciences by integrating radiological techniques with advanced machine learning methodologies to create a non-invasive, efficient, and less examiner-dependent approach to age estimation. Our study includes a new dataset of 12,827 dental panoramic X-ray images representing the Brazilian population, covering an age range from 2.25 to 96.50 years. To analyze these exams, we employed a model adapted from InceptionV4, enhanced with data augmentation techniques. The proposed approach achieved robust and reliable results, with a Test Mean Absolute Error of 3.1 years and an R-squared value of 95.5%. Professional radiologists have validated that our model focuses on critical features for age assessment used in odontology, such as pulp chamber dimensions and stages of permanent teeth calcification. Importantly, the model also relies on anatomical information from the mandible, maxillary sinus, and vertebrae, which enables it to perform well even in edentulous cases. This study demonstrates the significant potential of machine learning to revolutionize age estimation in Forensic Science, offering a more accurate, efficient, and universally applicable solution., (© 2024. The Author(s).)

Background: We aimed to determine the feasibility of utilizing deep learning-based predictions of the indications for cracked tooth extraction using panoramic radiography., Methods: Panoramic radiographs of 418 teeth (group 1: 209 normal teeth; group 2: 209 cracked teeth) were evaluated for the training and testing of a deep learning model. We evaluated the performance of the cracked diagnosis model for individual teeth using InceptionV3, ResNet50, and EfficientNetB0. The cracked tooth diagnosis model underwent fivefold cross-validation with 418 data instances divided into training, validation, and test sets at a ratio of 3:1:1., Results: To evaluate the feasibility, the sensitivity, specificity, accuracy, and F1 score of the deep learning models were calculated, with values of 90.43-94.26%, 52.63-60.77%, 72.01-75.84%, and 76.36-79.00%, respectively., Conclusion: We found that the indications for cracked tooth extraction can be predicted to a certain extent through a deep learning model using panoramic radiography., (© 2024. The Author(s).)

This study reviews the two most important and frequently used systems of tomography used in dentistry today. These are the dental panoramic radiograph (DPR) and cone-beam computed tomography (CBCT). The importance of the DPR has been accentuated by the recent COVID-19 pandemic, as it does not produce an aerosol. Its clinical importance is derived from its panoramic display of the jaws and associated structures and should be examined for incidental findings that may portend a potentially serious outcome. An important recent spin-off of the DPR is the extra-oral bitewing, which can replace its traditional, uncomfortable and aerosol-generating intra-oral counterpart. Although much has been written about them, this paper reviews their essential attributes and limitations in clinical dentistry. Although attempts have been made to reproduce some of the attributes of CT in CBCT such as Hounsfield Units (HU) and improve the contrast resolution of the soft tissues, these remain elusive. Nevertheless, CBCT's dataset should be appropriately reconstructed to fully display the clinical feature prompting its prescription. In certain cases, more than one mode of reconstruction is required.

Background and Aim: Dental caries is largely preventable, yet an important global health issue. Numerous systematic reviews have summarized the efficacy of artificial intelligence (AI) models for the diagnosis and detection of dental caries. Therefore, this umbrella review aimed to synthesize the results of systematic reviews on the application and effectiveness of AI models in diagnosing and detecting dental caries., Methods: MEDLINE/PubMed, IEEE Explore, Embase, and Cochrane Database of Systematic Reviews were searched to retrieve studies. Two authors independently screened the articles based on eligibility criteria and then, appraised the included articles. The findings are summarized in tabulation form and discussed using the narrative method., Result: A total of 1249 entries were identified out of which 7 were finally included. The most often employed AI algorithms were the multilayer perceptron, support vector machine (SVM), and neural networks. The algorithms were built to perform the segmentation, classification, caries detection, diagnosis, and caries prediction from several sources, including periapical radiographs, panoramic radiographs, smartphone images, bitewing radiographs, near-infrared light transillumination images, and so forth. Convoluted neural networks (CNN) demonstrated high sensitivity, specificity, and area under the curve in the caries detection, segmentation, and classification tests. Notably, AI in conjunction with periapical and panoramic radiography images yielded better accuracy in detecting and diagnosing dental caries., Conclusion: AI models, especially convolutional neural network (CNN)-based models, have an enormous amount of potential for accurate, objective dental caries diagnosis and detection. However, ethical considerations and cautious adoption remain critical to its successful integration into routine practice., (© 2024 The Author(s). Clinical and Experimental Dental Research published by John Wiley & Sons Ltd.)

Objectives: In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs., Methods: A total of 3854 mixed pediatric patients PRs were labelled for deciduous and permanent teeth using the CranioCatch labeling program. The dataset was divided into three subsets: training (n = 3093, 80% of the total), validation (n = 387, 10% of the total) and test (n = 385, 10% of the total). An artificial intelligence (AI) algorithm using YOLO-v5 models were developed., Results: The sensitivity, precision, F-1 score, and mean average precision-0.5 (mAP-0.5) values were 0.99, 0.99, 0.99, and 0.98 respectively, to teeth detection. The sensitivity, precision, F-1 score, and mAP-0.5 values were 0.98, 0.98, 0.98, and 0.98, respectively, to teeth segmentation., Conclusions: YOLO-v5 based models can have the potential to detect and enable the accurate segmentation of deciduous and permanent teeth using PRs of pediatric patients with mixed dentition., (© 2024. The Author(s).)

Background: The stage of tooth formation is one of the most reliable indicators for predicting a patient's developmental age by radiographs. This study compared the accuracy of three distinct dental age estimation methods (Demirjian, Nolla, and Willems) in children aged 3-17 in the northern Iranian population. Methods: This cross-sectional study examined panoramic radiographs of 434 children aged 3-17 from Mazandaran Province, Iran, who had teeth 31-37 present on the left mandible. This study employed the Demirjian, Nolla, and Willems methods to estimate the dental age of the sample and compare it with the chronological age. The data were analyzed using SPSS v16. A paired t -test was used to compare chronological and dental ages. The Pearson correlation was used to correlate the chronological and dental ages. The errors of different methods were compared using the Wilcoxon test. P values < 0.05 were considered significant for all tests except Wilcoxon. For Wilcoxon, a P value < 0.017 was considered significant. Results: The three methods presented differing mean estimated ages. The Demirjian method delivered the highest mean, and all three methods differed significantly when compared in pairs. The results showed that the Demirjian method overestimated chronological age by 0.25 years ( P < 0.001) in girls and 0.09 years ( P = 0.28) in boys. The Willems method underestimated chronological age by 0.05 years ( P = 0.47) in girls and 0.12 years ( P = 0.13) in boys. The Nolla method underestimated chronological age by 0.41 years ( P < 0.001) in girls and 0.40 years ( P < 0.001) in boys. The accuracy of each method varied with the patient's age. Conclusion: According to the findings, the Willems method outperformed the Demirjian method, and the Demirjian method exceeded the Nolla method for estimating dental age in Iranian children aged 3-17. Overall, the Demirjian method overestimated the age of the study population, whereas the other two underestimated it., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 Benjamin Pliska et al.)

Stroke is one of the major causes of death worldwide, and is closely associated with atherosclerosis of the carotid artery. Panoramic radiographs (PRs) are routinely used in dental practice, and can be used to visualize carotid artery calcification (CAC). The purpose of this study was to automatically and robustly classify and segment CACs with large variations in size, shape, and location, and those overlapping with anatomical structures based on deep learning analysis of PRs. We developed a cascaded deep learning network (CACSNet) consisting of classification and segmentation networks for CACs on PRs. This network was trained on ground truth data accurately determined with reference to CT images using the Tversky loss function with optimized weights by balancing between precision and recall. CACSNet with EfficientNet-B4 achieved an AUC of 0.996, accuracy of 0.985, sensitivity of 0.980, and specificity of 0.988 in classification for normal or abnormal PRs. Segmentation performances for CAC lesions were 0.595 for the Jaccard index, 0.722 for the Dice similarity coefficient, 0.749 for precision, and 0.756 for recall. Our network demonstrated superior classification performance to previous methods based on PRs, and had comparable segmentation performance to studies based on other imaging modalities. Therefore, CACSNet can be used for robust classification and segmentation of CAC lesions that are morphologically variable and overlap with surrounding structures over the entire posterior inferior region of the mandibular angle on PRs., (© 2024. The Author(s).)

Most artificial intelligence (AI) studies have attempted to identify dental implant systems (DISs) while excluding low-quality and distorted dental radiographs, limiting their actual clinical use. This study aimed to evaluate the effectiveness of an AI model, trained on a large and multi-center dataset, in identifying different types of DIS in low-quality and distorted dental radiographs. Based on the fine-tuned pre-trained ResNet-50 algorithm, 156,965 panoramic and periapical radiological images were used as training and validation datasets, and 530 low-quality and distorted images of four types (including those not perpendicular to the axis of the fixture, radiation overexposure, cut off the apex of the fixture, and containing foreign bodies) were used as test datasets. Moreover, the accuracy performance of low-quality and distorted DIS classification was compared using AI and five periodontists. Based on a test dataset, the performance evaluation of the AI model achieved accuracy, precision, recall, and F1 score metrics of 95.05%, 95.91%, 92.49%, and 94.17%, respectively. However, five periodontists performed the classification of nine types of DISs based on four different types of low-quality and distorted radiographs, achieving a mean overall accuracy of 37.2 ± 29.0%. Within the limitations of this study, AI demonstrated superior accuracy in identifying DIS from low-quality or distorted radiographs, outperforming dental professionals in classification tasks. However, for actual clinical application of AI, extensive standardization research on low-quality and distorted radiographic images is essential., (© 2024. The Author(s).)

Introduction: Accurate segmentation of the key mandibular region in the oral panoramic X-ray image is crucial for the diagnosis of the mandibular region and the planning of implant surgery. Because the oral panoramic X-ray image contains many important anatomical information for implant treatment evaluation. However, the fuzzy boundary between each region in the image makes the segmentation task very challenging. In data-driven segmentation methods, corresponding datasets are often required. Due to the limited oral data set at present, there is a bottleneck in clinical application., Materials and Methods: In this paper, we build a panoramic X-ray image dataset for the mandibular region. The dataset has a total of 711 images. The dataset is divided into 8 categories based on the number of teeth and treatment conditions. The annotations include mandible, normal teeth, treated teeth and implants. In terms of network segmentation. According to the local and global characteristics of the dataset, we designed a CBTrans partition network by paralleling the convolution block and the Swin-transform block of the bottleneck structure., Results: The experimental results show that our proposed network achieves excellent performance on the mandibular region segmentation dataset and the common retina dataset DRIVE., Conclusion: CBTrans can better extract features locally and globally by combining CNN of the bottleneck structure and Swin Transformer in parallel. CBTrans demonstrates performance advantages over other similar hybrid architecture models., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Zhang Jianguo reports financial support was provided by Collaborative Innovation Fund of Shanghai Institute of Technology. Hu Fengling reports financial support was provided by Youth Foundation of Zhongshan Hospital, Fudan University. Yu Youcheng reports financial support was provided by the Science and Technology Commission of Shanghai Municipality, Science, Technology and In-novation Action Plan., (Copyright © 2023 Elsevier Masson SAS. All rights reserved.)

Introduction: The aim of these four case reports was to illustrate the presence of potential upper second molar impactions associated with ectopic third molars and to highlight that some cases have an atypical radiographic presentation., Case Presentations: Four patients (age range = 7-12 years) with various malocclusions presented to the paediatric and orthodontic departments for treatment to address their presenting complaints. Incidental radiographic findings demonstrated potentially impacted upper second molars associated with ectopic third molars. In all of these cases, a joint paediatric-orthodontic approach was adopted to address their dental health, prevent upper second molar impaction and to treat their malocclusion., Discussion: Careful and systematic review of radiographic imaging was necessary in order to diagnose these cases correctly. These cases demonstrated that it was not always simple to determine impactions, particularly as identification of third molar crypts can be difficult. On occasion, sequential radiographic monitoring is advocated, particularly in patients in the mixed dentition; however, clinicians must be mindful of the risks of ionising radiation as it is not routine practice to irradiate a patient multiple times., Conclusion: The series of cases highlights the need for a systematic assessment of OPTs to identify ectopic upper third molars. The input from radiologists is invaluable and if necessary, supplemental three-dimensional cone-beam computed tomography can be performed., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Forensic age assessment in the living can provide legal certainty when an individual's chronological age is unknown or when age-related information is questionable. An established method involves assessing the eruption of mandibular third molars through dental panoramic radiographs (PAN). In age assessment procedures, the respective findings are compared to reference data. The objective of this study was to generate new reference data in line with the required standards for mandibular third molar eruption within a German population. For this purpose, 605 PANs from 302 females and 303 males aged 15.04 to 25.99 years were examined. The PANs were acquired between 2013 and 2020, and the development of the mandibular third molars was rated independently by two experienced examiners using the Olze et al. staging scale from 2012. In case of disagreement in the assigned ratings, a consensus was reached through arbitration. While the mean, median and minimum ages were observed to increase with each stage of mandibular third molar eruption according to the Olze method, there was considerable overlap in the distribution of age between the stages. The minimum age for stage D, which corresponds to complete tooth eruption, was 16.1 years for females and 17.1 years for males. Thus, the completion of mandibular third molar eruption was found in both sexes before reaching the age of 18. In all individuals who had at least one tooth with completed eruption and who were younger than 17.4 years of age (n = 10), mineralization of the teeth in question was not complete. Based on our findings, the feature of assessing mandibular third molar eruption in PAN cannot be relied upon for determining age of majority., (© 2024. The Author(s).)

Purpose: To analyze the visibility of the maxillary sinus septa (MSS) in panoramic radiography (PR) versus cone beam computed tomography (CBCT) and to investigate whether the buccal cortical bone thickness (BT) or the septa dimensions influence their visibility., Methods: Corresponding PR and CBCT images of 355 patients were selected and examined for MSS visibility. The septa dimensions (width, height, depth) and the BT were measured. Results were analysed statistically., Results: Comparing the corresponding regions on CBCT and PR, 170 MSS were identified; however, only 106 of these were also visible using PR. The MSS visibility was significantly higher on CBCT versus PR images (P1: p = 0.039, P2: p = 0.015, M1: p = 0.041, M2: p = 0.017, M3: p = 0.000), except region C (p = 0.625). Regarding the measurements of MSS dimensions, only the height in region M1 (p = 0.013) and the width in region P2 (p = 0.034) were significantly more visible on CBCT. The BT in the area of the MSS was found to have a marginal influence on its visibility on the PR images only in regions M3 and M1 (M3: p = 0.043, M1: p = 0.047). In terms of MSS visibility based on the dimensions, significance was found for all three influencing variables only in region P2 (width; p = 0.041, height; p = 0.001, depth; p = 0.007). There were only isolated cases of further significance: M3 for width (p = 0.043), M2 for height (p = 0.024), and P1 for depth (p = 0.034), no further significance was noted., Conclusion: MSS visibility appears significantly higher on CBCT versus PR images. It is concluded that the septa dimensions and BT can influence MSS visibility on PR images just in certain regions., (© 2024. The Author(s).)

Background/aim: The styloid process (SP) becomes clinically relevant when it shows enlargement (>30 mm) in the sense of an elongated SP (ESP) and/or increasing calcification leading to Eagle Syndrome (ES). Panoramic radiograph (PR) or computed tomography (CT) are part of the routine diagnostics in ES. Currently, CT is considered the gold standard. The aim of this study was to investigate the accuracy in the diagnostics/measurements of SP/ESP throughout a comparative study between PR and CT. Furthermore, in addition to measuring established parameters, this study aimed to determine the currently unexamined width in the base and tip of the SP., Patients and Methods: The present study examined the radiological findings of bilateral SP in 100 patients who received both PR and CT on the same day. Measurements of the length of the SP and width at the basis and tip were performed. Furthermore, calcification patterns, Langlais classification and the prevalence of ESP were analyzed., Results: There was a highly significant correlation between PR and CT measuring SP for every parameter. Males showed significantly longer SP than females among the age group between 18-75 years. The results of the length measurements of the SP (male: right SP=32.98 mm; left SP=35.21 mm; female: right SP=30.31 mm; left SP=30.92 mm) significantly exceeded the values of comparable studies., Conclusion: Consequently, it can be concluded that PR provides accurate measurements when compared to CT for measuring and diagnosing SP/ESP/Eagle syndrome. This study was one of the first to examine the width of the SP in the base and tip, thus these measurements can serve as a baseline for further studies. Since the mean lengths of SP exceeded 30.0 mm in the present study, these findings raise the question of whether the cut-off of 30.0 mm is adequate for the diagnosis of ESP., (Copyright © 2024, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Background: Panoramic radiography (PR) is available to determine the contact relationship between maxillary molar teeth (MMT) and the maxillary sinus floor (MSF). However, as PRs do not provide clear and detailed anatomical information, advanced imaging methods can be used., Aim: The aim of this study was to evaluate the diagnostic performance of deep learning (DL) applications that assess the relationship of the MSF to the first maxillary molar teeth (fMMT) and second maxillary molar teeth (sMMT) on PRs with data confirmed by cone beam computed tomography (CBCT)., Methods: A total of 2162 fMMT and sMMT were included in this retrospective study. The contact relationship of teeth with MSF was compared among DL methods., Results: DL methods, such as GoogLeNet, VGG16, VGG19, DarkNet19, and DarkNet53, were used to evaluate the contact relationship between MMT and MSF, and 85.89% accuracy was achieved by majority voting. In addition, 88.72%, 81.19%, 89.39%, and 83.14% accuracy rates were obtained in right fMMT, right sMMT, left fMMT, and left sMMT, respectively., Conclusion: DL models showed high accuracy values in detecting the relationship of fMMT and sMMT with MSF., (Copyright © 2024 Copyright: © 2024 Nigerian Journal of Clinical Practice.)

Objectives: To investigate the application value of combining the Demirjian's method with machine learning algorithms for dental age estimation in northern Chinese Han children and adolescents., Methods: Oral panoramic images of 10 256 Han individuals aged 5 to 24 years in northern China were collected. The development of eight permanent teeth in the left mandibular was classified into different stages using the Demirjian's method. Various machine learning algorithms, including support vector regression (SVR), gradient boosting regression (GBR), linear regression (LR), random forest regression (RFR), and decision tree regression (DTR) were employed. Age estimation models were constructed based on total, female, and male samples respectively using these algorithms. The fitting performance of different machine learning algorithms in these three groups was evaluated., Results: SVR demonstrated superior estimation efficiency among all machine learning models in both total and female samples, while GBR showed the best performance in male samples. The mean absolute error (MAE) of the optimal age estimation model was 1.246 3, 1.281 8 and 1.153 8 years in the total, female and male samples, respectively. The optimal age estimation model exhibited varying levels of accuracy across different age ranges, which provided relatively accurate age estimations in individuals under 18 years old., Conclusions: The machine learning model developed in this study exhibits good age estimation efficiency in northern Chinese Han children and adolescents. However, its performance is not ideal when applied to adult population. To improve the accuracy in age estimation, the other variables can be considered.

Purpose: To assess the impact of endoscope-assisted fractured roots or fragments extraction within the mandibular canal, along with quantitative sensory testing (QST) alterations in the inferior alveolar nerve (IAN)., Methods: Six patients with lower lip numbness following mandibular third molar extraction were selected. All patients had broken roots or fragments within the mandibular canal that were extracted under real-time endoscopic assistance. Follow-up assessments were conducted on postoperative days 1, 7, and 35, including a standardized QST of the lower lip skin., Results: The average surgical duration was 32.5 min, with the IAN exposed in all cases. Two of the patient exhibited complete recovery of lower lip numbness, three experienced symptom improvement, and one patient remained unaffected 35 days after the surgery. Preoperative QST results showed that the mechanical detection and pain thresholds on the affected side were significantly higher than those on the healthy side, but improved significantly by postoperative day 7 in five patients, and returned to baseline in two patients on day 35. There were no significant differences in the remaining QST parameters., Conclusions: All endoscopic surgical procedures were successfully completed without any additional postoperative complications. There were no cases of deterioration of IAN injury, and lower lip numbness recovered in the majority of cases. Endoscopy allowed direct visualization and examination of the affected nerve, facilitating a comprehensive analysis of the IAN., (© 2024. The Author(s).)

Background: The aim of the current study was to propose a classification of impacted cuspids, and bicuspids based on their spatial position in the maxillary and mandibular arches and their radiological appearance., Methods: This prospective study was conducted including all the patients who reported to our center for extractions, orthodontic treatment, missing permanent cuspids/bicuspids, removal of third molar teeth. All patients who reported within a time frame of 6 months were enrolled in the study. Orthopantomogram was advised for all the patients as a part of diagnostic work up. The panoramic radiographs and clinical data were reviewed and patients with evidence of impacted bicuspids and cuspids were included in the study and were advised to undergo a cone beam computed tomography to evaluate the 3-dimensional position of the impacted teeth. Treatment plan was decided based on the position of the impacted teeth and feasibility for orthodontic movement. Standard surgical protocol was followed for all the patients. The duration of the procedure from the time of incision till the suturing were tabulated and the difficulty of extraction was correlated with the current Difficulty Index to validate the same., Results: A total number of 4165 patients were enrolled in the study and based on the inclusion and exclusion criteria, 3680 patients were excluded and 487 patients with impacted cuspids/bicuspids were included in the study. A total number of 231 patients had impacted maxillary cuspids and bicuspids and 256 cases had impacted mandibular cuspids/bicuspids. Amongst the 256 mandibular cuspid and bicuspid that were impacted, 62% were in position C while the rest of 38% were in position B. More than ⅓ of the teeth which were in position C had transmigrated (35%). Seventy-three percent of the maxillary teeth were in position C and 69% of these teeth were either transmigrated (29%), or were horizontally (38%) placed in the alveolus or inverted (2%). The rest of the 31% were found to be either mesially inclined or vertical. The average Difficulty Index for maxillary teeth was 8 and mandibular teeth was 9. Mandibular cuspids and bicuspids in position B and C took longer time for surgical removal compared to the maxillary teeth and this was statistically significant., Conclusions: The proposed clinical classification on impacted mandibular cuspids and bicuspids provides a structured approach to plan the treatment based on the 3-dimensional position of the teeth in the arch.