AI-Powered Identification of Osteoporosis in Dental Panoramic Radiographs: Addressing Methodological Flaws in Current Research.

Background: Osteoporosis, a systemic skeletal disorder, is expected to affect 60% of women over 50. While dual-energy X-ray absorptiometry (DXA) scans are the current gold standard for diagnosis, they are typically used only after fractures occur, highlighting the need for early detection tools. Initial studies have shown panoramic radiographs (PRs) to be a potential medium, but these have methodological flaws. This study aims to address these shortcomings by developing a robust AI application for accurate osteoporosis identification in PRs. Methods: A total of 348 PRs were used for development, 58 PRs for validation, and 51 PRs for hold-out testing. Initially, the YOLOv8 object detection model was employed to predict the regions of interest. Subsequently, the predicted regions of interest were extracted from the PRs and processed by the EfficientNet classification model. Results: The model for osteoporosis detection on a PR achieved an overall sensitivity of 0.83 and an F1-score of 0.53. The area under the curve (AUC) was 0.76. The lowest detection sensitivity was for the cropped angulus region (0.66), while the highest sensitivity was for the cropped mental foramen region (0.80). Conclusion: This research presents a proof-of-concept algorithm showing the potential of deep learning to identify osteoporosis in dental radiographs. Furthermore, our thorough evaluation of existing algorithms revealed that many optimistic outcomes lack credibility when subjected to rigorous methodological scrutiny.

Do Tunisian children with unilateral cleft lip and palate differ from normal individuals in dental maturity? A pilot study.

INTRODUCTION: Cleft lip and palate (CLP) is the most common congenital malformation of the head and neck. Children with CLP often exhibit dental anomalies. AIM: To evaluate the dental age (DA) of unilateral CLP in Tunisian children. METHODS: This was a cross-sectional study carried out in the department of pediatric dentistry at the University Hospital La Rabta, Tunis. Patients aged between 5 and 14 years, with no other congenital anomalies or syndromes in the craniofacial region other than CLP, were included. The patients' chronological ages were first calculated in years and months. DA was assessed in panoramic radiographs using Demirjian's method. The score of each stage is allocated, and the sum of the scores provides an evaluation of the subject's dental maturity. RESULTS: Fifty-three patients were included in the present study. No difference was observed between the two groups regarding the dental age. A strong and positive correlation between the DA and the chronological age in the two groups was observed (r=0.826). Estimated regression showed that chronological age alone explained 57,4% (r2=0.574) of the dental age variation in the study group and 64.5% (r2=0.645) in the control group. CONCLUSION: For dental management, CLP children should have the same approach in orthodontics and pediatric dentistry as individuals without clefts, with a focus on the individualization of diagnosis and treatment planning.

Automatic feature segmentation in dental panoramic radiographs.

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.

Enhanced Osteoporosis Detection Using Artificial Intelligence: A Deep Learning Approach to Panoramic Radiographs with an Emphasis on the Mental Foramen.

Osteoporosis, a skeletal disorder, is expected to affect 60% of women aged over 50 years. Dual-energy X-ray absorptiometry (DXA) scans, the current gold standard, are typically used post-fracture, highlighting the need for early detection tools. Panoramic radiographs (PRs), common in annual dental evaluations, have been explored for osteoporosis detection using deep learning, but methodological flaws have cast doubt on otherwise optimistic results. This study aims to develop a robust artificial intelligence (AI) application for accurate osteoporosis identification in PRs, contributing to early and reliable diagnostics. A total of 250 PRs from three groups (A: osteoporosis group, B: non-osteoporosis group matching A in age and gender, C: non-osteoporosis group differing from A in age and gender) were cropped to the mental foramen region. A pretrained convolutional neural network (CNN) classifier was used for training, testing, and validation with a random split of the dataset into subsets (A vs. B, A vs. C). Detection accuracy and area under the curve (AUC) were calculated. The method achieved an F1 score of 0.74 and an AUC of 0.8401 (A vs. B). For young patients (A vs. C), it performed with 98% accuracy and an AUC of 0.9812. This study presents a proof-of-concept algorithm, demonstrating the potential of deep learning to identify osteoporosis in dental radiographs. It also highlights the importance of methodological rigor, as not all optimistic results are credible.

Prevalence and Pattern of Mandibular Condensing Osteitis Lesions in Saudi Population at Qassim Region.

Background: Condensing osteitis (CO) is a common radiopaque lesion observed in the jaws, often detected incidentally on panoramic radiographs. Understanding the prevalence and characteristics of CO is essential for early detection and appropriate management. Objective: To determine the prevalence and characteristics of condensing osteitis among the Saudi population in the Qassim region. Methods: A retrospective study was conducted using 876 digital panoramic radiographs. The presence of CO was identified based on specific radiographic features, and data were collected regarding gender, age, lesion localization, lesion shape, and associated dental status. Results: The prevalence of CO was found to be 2.3% (n = 20) in the study population, with a higher predilection in females (1.4%) compared to males (0.9%). The most commonly affected age group was 30-39 years for males and 10-19 and 30-39 years for females. The mandibular molar region was predominantly affected (90%), with a 'U' shape observed in 55% of the lesions. Root canal treatment was the most commonly associated dental status (75%), followed by deep caries (20%) and large restorations (5%). Conclusion: The study highlights a 2.3% prevalence of CO in the Saudi population of the Qassim region, with a higher predilection in females and a predominant localization in the mandibular molar region. Dental practitioners should be vigilant in identifying CO, especially in at-risk populations, to facilitate timely diagnosis and appropriate management.

Incidence and Pattern of Mandibular Impacted 3rd Molars and Their Relationship with the Mandibular Canal in Population of Jizan Province of Saudi Arabia: A Retrospective Radiographic Study.

Introduction: Impacted mandibular third molars pose challenges in dental practice, often requiring surgical intervention. This retrospective study aims to analyze the demographics, impaction patterns, and anatomical relationships of impacted mandibular third molars among Saudi patients. Methodology: Data from 722 patients visiting the Department of Maxillofacial Surgery and Diagnostic Sciences at Jizan University were retrospectively analyzed. Parameters including gender distribution, impaction types, relationship with the mandibular canal, and age demographics were evaluated based on panoramic radiographs. Results: Bilateral impaction predominated (57.59%), with mesioangular impaction being the most common (46.51%). Gender differences were noted in impaction types and relationships with the mandibular canal. Interruption of white lines of the canal was more frequent in males (70.00%). Early adulthood (20-25 years) exhibited the highest prevalence of impaction. Conclusion: The study provides insights into the demographics and characteristics of impacted mandibular third molars among Saudi patients. Gender-specific variations and age distribution underscore the importance of tailored treatment approaches and early intervention.

The Cayor Atlas: a black Senegalese dental atlas for permanent tooth formation.

INTRODUCTION: Many aspects of tooth development have been documented, particularly in Caucasian populations. However, dental development has not been extensively studied in West Africa. OBJECTIVE: The present study was designed to provide information on the sequences of tooth calcification in West African black Senegalese children and to compare the results with those of other populations, notably the London Atlas. METHODS: A total of 556 orthopantomograms (OPGs) from 289 males and 266 females with a mean age of 11.34 ± 3.84 years were analyzed. Demirjian A-H staging was applied to record the stages of tooth development. Tables of tooth development stages for each tooth were generated separately for age cohorts and by sex. The most common stage of tooth formation (modal) was the characteristic age stage of development. Differences between boys and girls and between maxillary and mandibular teeth were also analyzed using chi-squares. Accuracy was assessed by comparing the age estimated by the Dental Development Atlas for this population (Cayor Atlas) and the London Atlas tooth with chronological age using the Bland-Altman test. RESULTS: There was no significant difference in tooth development between girls and boys, p > 0.05. Maxillary teeth had similar dental development to mandibular teeth, p > 0.05. The Pearson correlation test showed a strong correlation between chronological age and the age estimated by the Cayor atlas, p < 0.001. The Bland-Altman test also showed greater accuracy than the London Atlas. CONCLUSION: These results show dental calcification sequences different from those of the London Atlas Tooth and the Witts Atlas (Atlas of Black South African Subjects).

YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition.

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.

Automatic detection of the third molar and mandibular canal on panoramic radiographs based on deep learning.

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.

Performance of Artificial Intelligence Models Designed for Automated Estimation of Age Using Dento-Maxillofacial Radiographs-A Systematic Review.

Automatic age estimation has garnered significant interest among researchers because of its potential practical uses. The current systematic review was undertaken to critically appraise developments and performance of AI models designed for automated estimation using dento-maxillofacial radiographic images. In order to ensure consistency in their approach, the researchers followed the diagnostic test accuracy guidelines outlined in PRISMA-DTA for this systematic review. They conducted an electronic search across various databases such as PubMed, Scopus, Embase, Cochrane, Web of Science, Google Scholar, and the Saudi Digital Library to identify relevant articles published between the years 2000 and 2024. A total of 26 articles that satisfied the inclusion criteria were subjected to a risk of bias assessment using QUADAS-2, which revealed a flawless risk of bias in both arms for the patient-selection domain. Additionally, the certainty of evidence was evaluated using the GRADE approach. AI technology has primarily been utilized for automated age estimation through tooth development stages, tooth and bone parameters, bone age measurements, and pulp-tooth ratio. The AI models employed in the studies achieved a remarkably high precision of 99.05% and accuracy of 99.98% in the age estimation for models using tooth development stages and bone age measurements, respectively. The application of AI as an additional diagnostic tool within the realm of age estimation demonstrates significant promise.

Orthodontic diagnosis rates based on panoramic radiographs in children aged 6-8 years: A retrospective study.

Objective: This study aimed to retrospectively analyze the prevalence of orthodontic problems and the proportion of patients who underwent orthodontic diagnosis among children aged 6 (n = 300), 7 (n = 400), and 8 (n = 400) years who had undergone panoramic radiography. Methods: Children were divided into five groups according to their chief complaint and consultation: conservative dentistry, oral and maxillofacial surgery, orthodontics, periodontics, and prosthodontics). Chief complaints investigated included first molar eruption, lack of space for incisor eruption, frequency of eruption problems, lack of space, impaction, supernumerary teeth (SNT), missing teeth, and ectropion eruption. The number of patients whose chief complaint was not related to orthodontics but had dental problems requiring orthodontic treatment was counted. The proportion of patients with orthodontic problems who received an orthodontic diagnosis was also examined. Results: Dental trauma and SNT were the most frequent chief complaints among the children. The proportion of patients with orthodontic problems increased with age. However, the orthodontic diagnosis rates based on panoramic radiographs among children aged 6, 7, 8 years were only 1.5% (6 years) and 23% (7 and 8 years). Conclusions: Accurate information should be provided to patient caregivers to correct misconceptions regarding the appropriateness of delaying orthodontic examination until permanent dentition is established.

Synthetic, non-person related panoramic radiographs created by generative adversarial networks in research, clinical, and teaching applications.

OBJECTIVES: Generative Adversarial Networks (GANs) can produce synthetic images free from personal data. They hold significant value in medical research, where data protection is increasingly regulated. Panoramic radiographs (PRs) are a well-suited modality due to their significant level of standardization while simultaneously displaying a high degree of personally identifiable data. METHODS: We produced synthetic PRs (syPRs) out of real PRs (rePRs) using StyleGAN2-ADA by NVIDIA©. A survey was performed on 54 medical professionals and 33 dentistry students. They assessed 45 radiological images (20 rePRs, 20 syPRs, and 5 syPRcontrols) as real or synthetic and interpreted a single-image syPR according to the image quality (0-10) and 14 different items (agreement/disagreement). They also rated the importance for the profession (0-10). A follow-up was performed for test-retest reliability with >10 % of all participants. RESULTS: Overall, the sensitivity was 78.2 % and the specificity was 82.5 %. For professionals, the sensitivity was 79.9 % and the specificity was 82.3 %. For students, the sensitivity was 75.5 % and the specificity was 82.7 %. In the single syPR-interpretation image quality was rated at a median of 6 and 11 items were considered as agreement. The importance for the profession was rated at a median score of 7. The Test-retest reliability yielded a value of 0.23 (Cohen's kappa). CONCLUSIONS: The study marks a comprehensive testing to demonstrate that GANs can produce synthetic radiological images that even health professionals can sometimes not differentiate from real radiological images, thereby being genuinely considered authentic. This enables their utilization and/or modification free from personally identifiable information. CLINICAL SIGNIFICANCE: Synthetic images can be used for university teaching and patient education without relying on patient-related data. They can also be utilized to upscale existing training datasets to improve the accuracy of AI-based diagnostic systems. The study thereby supports clinical teaching as well as diagnostic and therapeutic decision-making.

Fully automated deep learning approach to dental development assessment in panoramic radiographs.

BACKGROUND: Dental development assessment is an important factor in dental age estimation and dental maturity evaluation. This study aimed to develop and evaluate the performance of an automated dental development staging system based on Demirjian's method using deep learning. METHODS: The study included 5133 anonymous panoramic radiographs obtained from the Department of Pediatric Dentistry database at Seoul National University Dental Hospital between 2020 and 2021. The proposed methodology involves a three-step procedure for dental staging: detection, segmentation, and classification. The panoramic data were randomly divided into training and validating sets (8:2), and YOLOv5, U-Net, and EfficientNet were trained and employed for each stage. The models' performance, along with the Grad-CAM analysis of EfficientNet, was evaluated. RESULTS: The mean average precision (mAP) was 0.995 for detection, and the segmentation achieved an accuracy of 0.978. The classification performance showed F1 scores of 69.23, 80.67, 84.97, and 90.81 for the Incisor, Canine, Premolar, and Molar models, respectively. In the Grad-CAM analysis, the classification model focused on the apical portion of the developing tooth, a crucial feature for staging according to Demirjian's method. CONCLUSIONS: These results indicate that the proposed deep learning approach for automated dental staging can serve as a supportive tool for dentists, facilitating rapid and objective dental age estimation and dental maturity evaluation.

Association of mandibular canine impaction and anatomical factors: A multicentre retrospective panoramic study in Latin America.

INTRODUCTION: Mandibular canine impaction is infrequent in dental eruption anomalies and treatment is very challenging. The aim of this multicenter retrospective panoramic study in Latin America was to evaluate panoramic radiographic imaging characteristics of mandibular canine impaction (impaction area, mandibular base contact, transmigration, impaction height and sex) and their associations. MATERIAL AND METHODS: This cross-sectional study evaluated 212 digital panoramic radiographs from three radiological centres in Tingo Maria (Peru), Bogota and Tunja (Colombia). The study included children of both sexes with impacted mandibular canines. Mandibular alpha angle, contact with mandibular basal bone (MBB), impacted sector according to 10 sectors with an adaptation of the Ericson and Kurol method, presence of transmigration and the impacted height were measured and the relationship among these measures was analyzed. Fisher's exact test, Chi-square and binary logistic regression were used. (P<0.05). RESULTS: The mandibular canine impaction showed contact with the MBB (32.08%), dental transmigration (36.79%), mainly located at an apical (40.09%) and sub-apical (36.79%) level. Transmigration mainly occurred in sectors 6 (33.30%) and 10 (25.60%) (P<0.001). It was found that for each year of increase in age, the possibility of contact with the MBB decreased (ß=0.89, P=0.010), and as the alpha angle increased by one degree the probability of contact with the MBB decreased (ß=0.97, P=0.001) and the probability of transmigration increased (ß=1.05, P<0.001). CONCLUSIONS: One third of the impacted canines were in contact with the MBB, while another third presented dental transmigration and were mainly located apically and subapically of the incisor roots. These imaging features should be taken into account when planning orthodontic treatment.

Automatic and robust estimation of sex and chronological age from panoramic radiographs using a multi-task deep learning network: a study on a South Korean population.

Sex and chronological age estimation are crucial in forensic investigations and research on individual identification. Although manual methods for sex and age estimation have been proposed, these processes are labor-intensive, time-consuming, and error-prone. The purpose of this study was to estimate sex and chronological age from panoramic radiographs automatically and robustly using a multi-task deep learning network (ForensicNet). ForensicNet consists of a backbone and both sex and age attention branches to learn anatomical context features of sex and chronological age from panoramic radiographs and enables the multi-task estimation of sex and chronological age in an end-to-end manner. To mitigate bias in the data distribution, our dataset was built using 13,200 images with 100 images for each sex and age range of 15-80 years. The ForensicNet with EfficientNet-B3 exhibited superior estimation performance with mean absolute errors of 2.93 ± 2.61 years and a coefficient of determination of 0.957 for chronological age, and achieved accuracy, specificity, and sensitivity values of 0.992, 0.993, and 0.990, respectively, for sex prediction. The network demonstrated that the proposed sex and age attention branches with a convolutional block attention module significantly improved the estimation performance for both sex and chronological age from panoramic radiographs of elderly patients. Consequently, we expect that ForensicNet will contribute to the automatic and accurate estimation of both sex and chronological age from panoramic radiographs.

Clinical analysis of nonsyndromic oligodontia phenotypes.

OBJECTIVES: To provide references, this study investigated the clinical characteristics of patients with nonsyndromic oligodontia. METHODS: The information of 178 patients with oligodontia was collected, including histories, oral examinations, and panoramic radiographs. Tooth agenesis characteristics were calculated and evaluated. All the data were statistically analyzed with SPSS 24.0 software. RESULTS: No significant difference in the number of missing teeth was found between sexes nor between the right and left sides, and congenitally missing teeth affected the maxillary arch (P<0.05). The highest prevalence of tooth agenesis was observed in the mandibular second premolars. In the maxillary arch, the most common pattern of tooth agenesis was agenesis of the bilateral first and second premolars. The agenesis of the bilateral second premolars was observed in the mandibular arch. The prevalence of a symmetric pattern between the right and left quadrants was significantly higher than that of matched patterns between the maxillary and mandibular antagonistic quadrants. Approximately 16.85% of patients with nonsyndromic oligodontia were affected by other tooth-related anomalies. CONCLUSIONS: The common patterns of tooth agenesis were successfully identified in patients with nonsyndromic oligodontia. Dentists need to provide multidisciplinary treatments for patients with nonsyndromic oligodontia because of variations in occluding and full-mouth tooth agenesis patterns.

Efficacy of the methods of age determination using artificial intelligence in panoramic radiographs - a systematic review.

The aim of this systematic review is to analyze the literature to determine whether the methods of artificial intelligence are effective in determining age in panoramic radiographs. Searches without language and year limits were conducted in PubMed/Medline, Embase, Web of Science, and Scopus databases. Hand searches were also performed, and unpublished manuscripts were searched in specialized journals. Thirty-six articles were included in the analysis. Significant differences in terms of root mean square error and mean absolute error were found between manual methods and artificial intelligence techniques, favoring the use of artificial intelligence (p < 0.00001). Few articles compared deep learning methods with machine learning models or manual models. Although there are advantages of machine learning in data processing and deep learning in data collection and analysis, non-comparable data was a limitation of this study. More information is needed on the comparison of these techniques, with particular emphasis on time as a variable.

Artificial intelligence-based automated preprocessing and classification of impacted maxillary canines in panoramic radiographs.

OBJECTIVES: Automating the digital workflow for diagnosing impacted canines using panoramic radiographs (PRs) is challenging. This study explored feature extraction, automated cropping, and classification of impacted and nonimpacted canines as a first step. METHODS: A convolutional neural network with SqueezeNet architecture was first trained to classify two groups of PRs (91with and 91without impacted canines) on the MATLAB programming platform. Based on results, the need to crop the PRs was realized. Next, artificial intelligence (AI) detectors were trained to identify specific landmarks (maxillary central incisors, lateral incisors, canines, bicuspids, nasal area, and the mandibular ramus) on the PRs. Landmarks were then explored to guide cropping of the PRs. Finally, improvements in classification of automatically cropped PRs were studied. RESULTS: Without cropping, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve for classifying impacted and nonimpacted canine was 84%. Landmark training showed that detectors could correctly identify upper central incisors and the ramus in ∼98% of PRs. The combined use of the mandibular ramus and maxillary central incisors as guides for cropping yielded the best results (∼10% incorrect cropping). When automatically cropped PRs were used, the AUC-ROC improved to 96%. CONCLUSIONS: AI algorithms can be automated to preprocess PRs and improve the identification of impacted canines.

The Utility of Cone Beam Computed Tomography Scans in Diagnosing and Treating Anterior Lesions of the Maxilla and Mandible.

BACKGROUND: The standard screening protocol for radiographic examination in dentistry as per the American Dental Association recommendations is a panoramic radiograph (PAN) and four horizontal bitewings. PAN inherently suffers from several shortcomings like the superimposition of anatomic structures, especially of the cervical spine that obscures a significant portion of the anterior maxilla and mandible. This region has a significant amount of pathology that is not adequately imaged. Three-dimensional (3D) imaging provides circumferential information on the area of interest and adds value to the diagnosis and treatment planning of pathology, especially in the anterior maxilla and mandible. However, there is not an adequate number of well-designed studies that articulate the true value addition of 3D imaging for the evaluation of this region. OBJECTIVES: The objective of this study is to evaluate the value addition of 3D imaging in diagnosing pathologies in the anterior maxilla and mandible when compared to two-dimensional PAN. MATERIALS AND METHODS: A total of 25 cases that had a diagnosis of anterior pathology and had both a PAN and a cone beam computed tomography (CBCT) scan were collected for this study. An institutional review board approval to retrospectively evaluate these data was obtained. The PAN and CBCT scans were randomly evaluated by a second-year dental student, an oral and maxillofacial radiology resident in training, and a board-certified oral radiologist. The scans were evaluated using a three-point modified Likert scale, where 1 represents "not visible or clear," 2 represents "visible but not clear," and 3 represents "visible and clear." The lesions were evaluated for characteristics like lesion location, size & shape, internal contents, borders of the lesion, cortical integrity, locularity, and effect on adjacent structures like root resorption. After the evaluation was completed, a comparison of the lesion diagnosis was done with histopathology to confirm the diagnosis. The evaluators were also asked to comment on the specific feature that 3D imaging provided that added value to the case. Kappa analysis was done to evaluate inter-operator reliability. RESULTS: PAN demonstrated significantly lower efficacy in identifying and diagnosing lesions. Only 56% of cases were analyzed using PAN, with 44% deemed undetectable or poorly visualized. These challenging cases necessitated CBCT scans for accurate diagnosis, which successfully diagnosed all 25 cases. The p-value of 0.0002 for PAN implies a highly significant difference from histopathology, suggesting the distinctions are not due to chance. Conversely, the p-value of 0.3273 for CBCT implies that observed differences may be random, lacking sufficient evidence to reject the null hypothesis. CBCT scans consistently outperformed PAN in visualizing various lesion characteristics, underscoring their superior diagnostic capabilities. CONCLUSIONS: In this study, with a small sample size, 3D imaging provided a significant value addition to the diagnosis and treatment planning by providing additional information regarding the location, extent, internal content, and effect on adjacent structures. The practical implications for clinical settings, along with comparisons to current literature, underscore the study's distinctiveness.

Correlation between the presence of tonsilloliths and the bone defects by periodontitis on imaging analysis: a pilot study.

BACKGROUND: Very recently, a significant relationship between tonsilloliths and dental plaque-related pathologies was reported using digital panoramic radiographs. Their dynamics over time suggest that tonsilloliths may be in a permanently active phase that functions to remove foreign matter. The aim of the study was to evaluate the relationship between the occurrence of tonsilloliths and the extent of periodontitis. METHODS: A total of 608 patients who underwent both CT and panoramic radiographs were included in the study. Both of two imaging were retrospectively and independently assessed with respect to the presence of tonsilloliths detected on CT and panoramic radiographs, and bone defects caused by periodontitis detected on panoramic radiographs. The type of retrospective study is case-control. Then, the differences between age groups were evaluated with respect to the degree of bone resorption and its correlation with the presence of tonsilloliths. The relationships between categorical variables were assessed using Pearson's correlation coefficient or Spearman's correlation coefficient. RESULTS: There was a significant relationship between tonsilloliths on CT and the extent of the bone defect on panoramic radiographs (Spearman's correlation coefficient, r = 0.648, p = 0.043). In addition, there was a significant difference in the extent of the bone defect caused by periodontitis between subjects with and without tonsilloliths in the 60 to 69-year-old group (Mann-Whitney U test, p = 0.025), 70 to 79-year-old group (Mann-Whitney U test, p = 0.002), and 80 to 89-year-old group (Mann-Whitney U test, p = 0.022), but not in other age groups (Mann-Whitney U test: under 9-year-old group, p = 1.000; 10 to 19-year-old group, p = 1.000; 20 to 29-year-old group, p = 0.854; 30 to 39-year-old group, p = 0.191, 40 to 49-year-old group, p = 0.749; 50 to 59-year-old group, p = 0.627; ≥90-year-old group, p = 1.000). CONCLUSIONS: The presence of tonsilloliths was related to the extent of periodontitis because the structures were responding dynamically.