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Despite the increased global mobility owing to the development of the international community, there remains a need for population-specific methods to estimate children's ages. Therefore, this study aimed to substantiate the necessity for a Japanese-specific age estimation method by contrasting the formerly reported age estimation accuracy and bias using Demirjian's method for Chinese, Taiwanese, South Korean, and Japanese children. We analyzed 1,558 panoramic radiographs from Japanese children (777 boys and 781 girls), assessed the maturity of seven left permanent teeth using Demirjian's criteria, and calculated the estimated age using Demirjian's method. The accuracy of the estimated ages was compared with previous reports of children from other East Asian countries which used the same age estimation method. Chinese, Taiwanese, and South Korean boys and girls were all reported to be older than their chronological ages, with the Eastern and Northern Chinese regions being the only exceptions. The same was true for Japanese children: the estimated ages of both sexes tended to be higher than their chronological age. However, there were significant variations in the values, indicating the differences in tooth growth and development between East Asian countries and sexes. Therefore, new regression equations specific to the Japanese population were formulated, and their accuracy was evaluated as the final result of this study.

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Age estimation plays a crucial role in various fields, including forensic science and anthropology. This study aims to develop and validate DentAge, a deep-learning model for automated age prediction using panoramic dental X-ray images. DentAge was trained on a dataset comprising 21,007 panoramic dental X-ray images sourced from a private dental center in Slovenia. The dataset included subjects aged 4 to 97 years with various dental conditions. Transfer learning was employed, initializing the model with ImageNet weights and fine-tuning on the dental image dataset. The model was trained using stochastic gradient descent with momentum, and mean absolute error (MAE) served as the objective function. Across the test dataset, DentAge achieved an MAE of 3.12 years, demonstrating its efficacy in age prediction. Notably, the model performed well across different age groups, with MAEs ranging from 1.94 (age group [10-20]) to 13.40 years (age group [90-100]). Visual evaluation revealed factors contributing to prediction errors, including prosthetic restorations, tooth loss, and bone resorption. DentAge represents a significant advancement in automated age prediction within dentistry. The model's robust performance across diverse age groups and dental conditions underscores its potential utility in real-world scenarios. Our model will be accessible to the public for further adjustments and validation, ensuring DentAge's effectiveness and trustworthiness in practical scenarios.

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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.

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Background  Dental caries is one of the most prevalent conditions in dentistry worldwide. Early identification and classification of dental caries are essential for effective prevention and treatment. Panoramic dental radiographs are commonly used to screen for overall oral health, including dental caries and tooth anomalies. However, manual interpretation of these radiographs can be time-consuming and prone to human error. Therefore, an automated classification system could help streamline diagnostic workflows and provide timely insights for clinicians. Methods This article presents a deep learning-based, custom-built model for the binary classification of panoramic dental radiographs. The use of histogram equalization and filtering methods as preprocessing techniques effectively addresses issues related to irregular illumination and contrast in dental radiographs, enhancing overall image quality. By incorporating three separate panoramic dental radiograph datasets, the model benefits from a diverse dataset that improves its training and evaluation process across a wide range of caries and abnormalities. Results The dental radiograph analysis model is designed for binary classification to detect the presence of dental caries, restorations, and periapical region abnormalities, achieving accuracies of 97.01%, 81.63%, and 77.53%, respectively. Conclusions The proposed algorithm extracts discriminative features from dental radiographs, detecting subtle patterns indicative of tooth caries, restorations, and region-based abnormalities. Automating this classification could assist dentists in the early detection of caries and anomalies, aid in treatment planning, and enhance the monitoring of dental diseases, ultimately improving and promoting patients' oral healthcare.

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BACKGROUND: The study evaluates the feasibility of employing the radiographic visibility of the root pulp and periodontal ligament in mandibular molars for age estimation, particularly focusing on the 18 years of age threshold. This study additionally investigates the potential of root canal width reduction in mandibular molars, as a reliable method for forensic age estimation in living individuals. MATERIALS AND METHODS: A cross-sectional study was conducted to assess the radiographic visibility of the root pulp (RPV) and the root canal width (RCW) of mandibular first, second, and third molars along with the radiographic visibility of the periodontal ligament (PLV) of mandibular third molars, in a sample of 403 individuals aged 16-25 years (220 males and 183 females). Data regarding age for different stages of RPV and PLV and various types of RCW were recorded and observed for sex-based differences. Results obtained were tabulated and descriptive statistics were applied to summarise the findings. RESULTS: Individuals over 18 years old were classified with higher accuracy using stage 3 of the RPV scoring system in all mandibular molars (first, second, and third) compared to stage 2, which was also effective for the second and third molars. This result held regardless of sex and side examined. Additionally, root canal width (RCW) assessment demonstrated that individuals with RCW types A, B, and C were more likely to be under 18 years old in both sexes. Conversely, individuals with RCW type U on the right side for males and the left side for females exhibited a higher likelihood of being above 18 years old. CONCLUSION: The study suggests that the assessment of mandibular molars could potentially serve as an auxiliary tool in age estimation methods, particularly for approximating individuals around the 18 years of age threshold. Further investigation is warranted to explore the potential application of root canal width measurements in forensic age estimation.

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OBJECTIVES: The purpose of this study was to generate radiographs including dentigerous cysts by applying the latest generative adversarial network (GAN; StyleGAN3) to panoramic radiography. METHODS: A total of 459 cystic lesions were selected, and 409 images were randomly assigned as training data and 50 images as test data. StyleGAN3 training was performed for 500 000 images. Fifty generated images were objectively evaluated by comparing them with 50 real images according to four metrics: Fréchet inception distance (FID), kernel inception distance (KID), precision and recall, and inception score (IS). A subjective evaluation of the generated images was performed by three specialists who compared them with the real images in a visual Turing test. RESULTS: The results of the metrics were as follows: FID, 199.28; KID, 0.14; precision, 0.0047; recall, 0.00; and IS, 2.48. The overall results of the visual Turing test were 82.3%. No significant difference was found in the human scoring of root resorption. CONCLUSIONS: The images generated by StyleGAN3 were of such high quality that specialists could not distinguish them from the real images.

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OBJECTIVE: Apical periodontitis (AP) is one of the most common pathologies of the oral cavity. An early and accurate diagnosis of AP lesions is crucial for proper management and planning of endodontic treatments. This study investigated the diagnostic accuracy of periapical radiography (PR) and panoramic radiography (PAN) in the detection of clinically/surgically/histopathologically confirmed AP lesions. METHOD: A systematic literature review was conducted in accordance with the PRISMA guidelines. The search strategy was limited to English language articles via PubMed, Embase and Web of Science databases up to June 30, 2023. Such articles provided diagnostic accuracy values of PR and/or PAN in the detection of AP lesions or alternatively data needed to calculate them. RESULTS: Twelve studies met inclusion criteria and were considered for the analysis. The average value of diagnostic accuracy in assessing AP lesions was 71% for PR and 66% for PAN. According to different accuracy for specific anatomical areas, it is recommended to use PR in the analysis of AP lesions located in the upper arch and lower incisor area, whereas lower premolar and molar areas may be investigated with the same accuracy with PR or PAN. CONCLUSIONS: Two-dimensional imaging must be considered the first-level examination for the diagnosis of AP lesions. PR had an overall slightly higher diagnostic accuracy than PAN. Evidence from this review provided a useful tool to support radiologists and dentists in their decision-making when inflammatory periapical bone lesions are suspected to achieve the best clinical outcome for patients, improving the quality of clinical practice.

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This study aimed to compare and evaluate the accuracy of the Demirjian (DE) and the London Atlas (LAE) dental age estimation methods in a Saudi population sample. This retrospective cross-sectional study used digital radiographs from electronic health records in three different dental institutes. In total, 357 male and 354 female (ages 5-15 years) digital orthopantomograms were selected for age estimation. The mean difference between the chronological age (CA) and age estimation method among males and females was 0.03 ± 0.34 and 0.00 ± 0.34, respectively, for LAE and 0.55 ± 0.84 and 0.76 ± 0.51, respectively, for DE. The mean difference between the LAE and DE methods among males and females was 0.52 ± 0.89 and -0.76 ± 0.57, respectively. No statistically significant difference between CA and LAE was found in either males (p = 0.079) or females (p = 0.872). A statistically significant difference was found between CA and DE in both genders (p < 0.001). A statistically significant difference was found between the LAE and DE groups (p < 0.001) in both genders. An overestimation of dental age was observed with DE compared with that in CA. LAE showed higher accuracy than CA, with no clinically significant difference. Although the difference between the LAE and DE methods was insignificant, the LAE method proved to be more accurate.

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BACKGROUND: This study aims to evaluate the performance of a deep learning system for the evaluation of tooth development stages on images obtained from panoramic radiographs from child patients. METHODS: The study collected a total of 1500 images obtained from panoramic radiographs from child patients between the ages of 5 and 14 years. YOLOv5, a convolutional neural network (CNN)-based object detection model, was used to automatically detect the calcification states of teeth. Images obtained from panoramic radiographs from child patients were trained and tested in the YOLOv5 algorithm. True-positive (TP), false-positive (FP), and false-negative (FN) ratios were calculated. A confusion matrix was used to evaluate the performance of the model. RESULTS: Among the 146 test group images with 1022 labels, there were 828 TPs, 308 FPs, and 1 FN. The sensitivity, precision, and F1-score values of the detection model of the tooth stage development model were 0.99, 0.72, and 0.84, respectively. CONCLUSIONS: In conclusion, utilizing a deep learning-based approach for the detection of dental development on pediatric panoramic radiographs may facilitate a precise evaluation of the chronological correlation between tooth development stages and age. This can help clinicians make treatment decisions and aid dentists in finding more accurate treatment options.

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Rare genetic diseases are difficult to diagnose and this translates in patient's diagnostic odyssey! This is particularly true for more than 900 rare diseases including orodental developmental anomalies such as missing teeth. However, if left untreated, their symptoms can become significant and disabling for the patient. Early detection and rapid management are therefore essential in this context. The i-Dent project aims to supply a pre-diagnostic tool to detect rare diseases with tooth agenesis of varying severity and pattern. To identify missing teeth, image segmentation models (Mask R-CNN, U-Net) have been trained for the automatic detection of teeth on patients' panoramic dental X-rays. Teeth segmentation enables the identification of teeth which are present or missing within the mouth. Furthermore, a dental age assessment is conducted to verify whether the absence of teeth is an anomaly or a characteristic of the patient's age. Due to the small size of our dataset, we developed a new dental age assessment technique based on the tooth eruption rate. Information about missing teeth is then used by a final algorithm based on the agenesis probabilities to propose a pre-diagnosis of a rare disease. The results obtained in detecting three types of genes (PAX9, WNT10A and EDA) by our system are very promising, providing a pre-diagnosis with an average accuracy of 72 %.

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A panorama ensures a stunning wide-angle field of view up to 360° representation of a scene, exceeding the limits of a normal photograph. Panoramic cameras satisfy the single-viewpoint characteristic. There are several types of panoramic cameras for 360-degree imaging. Multi-camera panoramic imaging systems pose a difficulty in obtaining a single projection center for the cameras. In a variety of practical implementations of panoramic cameras, it is possible to calculate three-dimensional coordinates from a panoramic image, especially using the Direct Linear Transformation (DLT) method. In this study, not only a defining method of the non-uniform image coordinate system is presented by utilizing the C-Means algorithm for a single panoramic image, captured with a Ladybug2 panoramic camera in a panoramic calibration room but also the use of an elliptical panoramic projection coordinate system is defined by the Singular Value Decomposition method in a panoramic view. The results of the suggested method have been compared with the DLT algorithm for a single panoramic image which defined a conventional photogrammetric image coordinate system. It has been observed that the proposed method provides more accurate results for the 3D coordinate definition.

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OBJECTIVES: To determine the genetic effects of panoramic radiography on the epithelial cells of the buccal mucosa by examining the micronucleus formation in these cells. MATERIALS AND METHODS: In this cross-sectional study, exfoliative cytology samples were prepared from the buccal mucosa of 36 patients immediately before and 10 days after panoramic radiography. The samples were prepared using liquid-based cytology with Papanicolaou staining. The slides were simultaneously evaluated by two expert pathologists and the ratio of the number of cells with micronuclei to the total number of cells on the slide was reported as a percentage. Data analysis was done using paired-samples T test, Pearson's correlation coefficient, and covariance analysis (α = 0.05). RESULTS: The study sample consisted of 24 (66.67%) males and 12 females (33.33%) with a mean (SD) age of 27.36 (8.19) years. The frequency of cells with micronucleus before and after panoramic radiography was not statistically different (p = 0.468). Additionally, the frequency of micronucleated cells was not correlated with age (p = 0.737) and sex (p = 0.211). CONCLUSION: Panoramic exposure slightly increased the frequency of cells with micronucleus in epithelial cells of the buccal mucosa. However, this increase was not statistically significant.

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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.

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Cleidocranial dysplasia (CCD) is a rare, congenital disorder characterized by a unique constellation of skeletal and dental abnormalities. The imaging findings, combined with clinical examination, help establish a definitive diagnosis. Understanding the broad spectrum of manifestations in CCD is essential for effective management and treatment. This case report aims to provide a comprehensive overview of a 25-year-old male patient with CCD, highlighting the genetic etiologies, clinical presentation, radiological findings, and a review of current literature to enhance understanding and awareness of this rare condition.

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The current study aimed to systematically review the literature on the accuracy of artificial intelligence (AI) models for osteoporosis (OP) diagnosis using dental images. A thorough literature search was executed in October 2022 and updated in November 2023 across multiple databases, including PubMed, Scopus, Web of Science, and Google Scholar. The research targeted studies using AI models for OP diagnosis from dental radiographs. The main outcomes were the sensitivity and specificity of AI models regarding OP diagnosis. The "meta" package from the R Foundation was selected for statistical analysis. A random-effects model, along with 95% confidence intervals, was utilized to estimate pooled values. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was employed for risk of bias and applicability assessment. Among 640 records, 22 studies were included in the qualitative analysis and 12 in the meta-analysis. The overall sensitivity for AI-assisted OP diagnosis was 0.85 (95% CI, 0.70-0.93), while the pooled specificity equaled 0.95 (95% CI, 0.91-0.97). Conventional algorithms led to a pooled sensitivity of 0.82 (95% CI, 0.57-0.94) and a pooled specificity of 0.96 (95% CI, 0.93-0.97). Deep convolutional neural networks exhibited a pooled sensitivity of 0.87 (95% CI, 0.68-0.95) and a pooled specificity of 0.92 (95% CI, 0.83-0.96). This systematic review corroborates the accuracy of AI in OP diagnosis using dental images. Future research should expand sample sizes in test and training datasets and standardize imaging techniques to establish the reliability of AI-assisted methods in OP diagnosis through dental images.

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Dental abnormalities are often detected in childhood and are reported to occur with high prevalence in patients who have undergone cancer treatment or chemotherapy. We performed a literature search of PubMed from 2004 to 2024 using the terms "dental anomaly" and "panoramic examination", and 298 potentially relevant articles were found. Thirty-one articles about dental abnormalities matched the eligibility criteria and were extracted for this review. Although the prevalence of tooth agenesis and microdontia in the general population was reported to be approximately 10% and 3%, respectively, the prevalence in patients who had undergone cancer treatment or chemotherapy was higher in all surveys, suggesting that the treatment is related to the occurrence of dental abnormalities. It is important to continue long-term follow-up with patients not only during treatment but also after the completion of treatment. Dental professionals should provide information about dental abnormalities to patients, their guardians, and medical professionals, which may lead to improvement in the quality of life of patients.

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Osteoporosis is a common systemic bone disorder in the elderly, characterized by low bone mineral density and deterioration of bone structure. Apical periodontitis is an inflammatory response to the microbial infection of root canals, typically characterized by apical bone destruction surrounding the tooth's apex. This systematic review aimed to determine if osteoporosis affects the prevalence of apical periodontitis in adults. PRISMA guidelines have been followed. It included randomized clinical trials, cross-sectional, cohort, and case-control studies, and excluded non-relevant investigations and various secondary sources. A comprehensive search was performed in PubMed, Scopus, and Web of Science, until 13 March 2024. The Newcastle-Ottawa Scale was used to assess the quality of the three selected studies: two cross-sectional studies and one case-control study. One investigation only included post-menopausal women recruited at a dental university clinic, the other integrated data from the total hospital patients' population, and the third selected patients referred to the university dental clinic from the university hospital. The findings varied: one study noted a marginal association between low bone mineral density and apical periodontitis, another found a significant association, and the third, with the lowest risk of bias, reported no link. The main limitations were the scarcity of eligible studies and their overall quality. The review was registered in the PROSPERO database (CRD42024523705), applied strict inclusion criteria and thorough searches by experienced and independent reviewers. There is no strong evidence that adult individuals with osteoporosis have a higher probability of developing apical periodontitis. However, clinicians should remain cautious of osteoporosis's potential impact on apical periodontitis development.

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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.

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BACKGROUND: This study aimed to develop a new formula to easily estimate the vertical dimension of occlusion (VDO) by using the distance between the mental foramen on a panoramic radiograph. SUBJECTS AND METHODS: A total of 508 dentulous subjects were selected from outpatient dental clinics at the College of Dental Medicine, Al-Azhar University. The vertical dimension of the occlusion was measured using a single calibrated calliper. For each subject, a digital panoramic radiograph was taken with fixed exposure parameters. The intermental foramina distance (IMFD) was measured. The data were collected and then analysed using the IBM SPSS version 20.0 software package. (Armonk, NY: IBM Corp.). Linear regression was used to determine the relationship between the intermental foramina distance (IMFD) and the vertical dimension at occlusion (VDO). RESULTS: Pearson's correlation analysis revealed that there was a strong correlation between the intermental foramina distance (IMFD) and the VDO. Thus, a novel formula was developed for determining the VDO using panoramic radiography. CONCLUSION: The novel formula developed herein facilitated the determination of the VDO among prosthetic rehabilitation for subjects who lost vertical dimension due to loss of posterior teeth or severe wear of natural posterior teeth. Further studies are needed to determine the clinical applicability of the derived formulae for edentulous subjects.

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Objective: This systematic review aims to summarize the evidence on the use and applicability of AI in impacted mandibular third molars. Methods: Searches were performed in the following databases: PubMed, Scopus, and Google Scholar. The study protocol is registered at the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY202460081). The retrieved articles were subjected to an exhaustive review based on the inclusion and exclusion criteria for the study. Articles on the use of AI for diagnosis, treatment, and treatment planning in patients with impacted mandibular third molars were included. Results: Twenty-one articles were selected and evaluated using the Scottish Intercollegiate Guidelines Network (SIGN) evidence quality scale. Most of the analyzed studies dealt with using AI to determine the relationship between the mandibular canal and the impacted mandibular third molar. The average quality of the articles included in this review was 2+, which indicated that the level of evidence, according to the SIGN protocol, was B. Conclusions: Compared to human observers, AI models have demonstrated decent performance in determining the morphology, anatomy, and relationship of the impaction with the inferior alveolar nerve canal. However, the prediction of eruptions and future horizons of AI models are still in the early developmental stages. Additional studies estimating the eruption in mixed and permanent dentition are warranted to establish a comprehensive model for identifying, diagnosing, and predicting third molar eruptions and determining the treatment outcomes in the case of impacted teeth. This will help clinicians make better decisions and achieve better treatment outcomes.