INTRODUCTION:Calcium silicate sealer (CSS) based obturation (SBO) has gained wide popularity for its relative simplicity and material biocompatibility. Limited information exists how its treatment outcome compares to warm-vertical compaction (WVC). The primary aim of this randomized controlled clinical trial was to compare the outcome of nonsurgical root canal treatment using SBO with CSS versus WVC with a resin-based sealer. The secondary aim was to assess differences in the obturation time between SBO and WVC. METHODS:A total of 195 participants with 212 teeth took part in this study and randomly allocated to either SBO or WVC after completion of the bio-mechanical instrumentation. The time required to complete the obturation was recorded. Participants were followed-up after a minimum of 12 months for clinical and radiographic assessment using periapical radiographs with the periapical index (PAI) and cone-beam computed tomography (CBCT) scans using the CBCT-PAI. Statistical evaluation involved descriptive analysis and binary logistic regression. RESULTS:One hundred eighty-one teeth in 167 participants were followed-up (85.4%) after 12-22 months (mean 12.9 months). Using strict criteria, success rates were 76.6% for SBO and 80.5% for WVC based on PAI, and 71.3% for SBO and 65.5% for WVC using CBCT-PAI. The overall success was 78.5% assessed using PA radiographs and 68.5% using CBCT with no significant differences in outcomes. SBO required significantly less time (85.4 ± 44.0s) to complete the root filling compared to WVC (159.7 ± 71.0s) (P < .001). CONCLUSIONS:Given comparable clinical outcomes to WVC yet demonstrating faster obturation time, SBO with CSS may be a suitable clinical alternative.

OBJECTIVES:To establish cephalometric norms for African American adults with normal occlusion and balanced soft tissue profiles in the Greater Philadelphia region and compare these findings with existing African American norms. MATERIALS AND METHODS:A total of 650 orthodontic records from adult African American subjects were reviewed. Normal occlusion was defined based on Angle's class I molar relationship, an overbite of 20-30% or greater than 0 mm and less than 3 mm, an overjet ranging from 1-3 mm, absence of crossbites, minor dental crowding, and gaps or rotations not exceeding 2 mm, along with a balanced facial profile. According to these criteria, 34 lateral cephalograms (25 females, 9 males; mean age 28.4 ± 12.7 years) were selected. These lateral cephalograms were digitally traced using Dolphin Imaging software (version 12.0, Chatsworth, CA, USA), and the obtained cephalometric measurements were compared with established African American norms from existing literature. RESULTS:Skeletally, African American subjects from the Greater Philadelphia region demonstrated smaller vertical measurements, characterized by reduced SN-GoGn, FMA, and Y‑axis angles compared to previously published norms for the African American population. The skeletal sagittal relationship indicated a more anteriorly positioned maxilla relative to established Caucasian norms. Dental evaluations revealed a slight increase in upper incisor inclination and a reduced interincisal angle, as evidenced by measurements such as the 1/to SN, 1/to FH, and 1/to NA angles when compared to existing African American norms. Additionally, subjects from the Greater Philadelphia region exhibited a more protrusive lower lip compared to previously reported norms for African Americans. CONCLUSION:Our findings indicate that cephalometric norms vary by both ethnicity and geographic region, underscoring the necessity of establishing population-specific standards to ensure accurate diagnosis and effective treatment planning.

The integration of artificial intelligence (AI) into education is transforming learning across various domains, including dentistry. Endodontic education can significantly benefit from AI chatbots; however, knowledge gaps regarding their potential and limitations hinder their effective utilization. This narrative review aims to: (A) explain the core functionalities of AI chatbots, including their reliance on natural language processing (NLP), machine learning (ML), and deep learning (DL); (B) explore their applications in endodontic education for personalized learning, interactive training, and clinical decision support; (C) discuss the challenges posed by technical limitations, ethical considerations, and the potential for misinformation. The review highlights that AI chatbots provide learners with immediate access to knowledge, personalized educational experiences, and tools for developing clinical reasoning through case-based learning. Educators benefit from streamlined curriculum development, automated assessment creation, and evidence-based resource integration. Despite these advantages, concerns such as chatbot hallucinations, algorithmic biases, potential for plagiarism, and the spread of misinformation require careful consideration. Analysis of current research reveals limited endodontic-specific studies, emphasizing the need for tailored chatbot solutions validated for accuracy and relevance. Successful integration will require collaborative efforts among educators, developers, and professional organizations to address challenges, ensure ethical use, and establish evaluation frameworks.

INTRODUCTION:Tooth fractures are associated with various etiological factors, including occlusal stress. While science has shown associations between maximum bite force and craniofacial skeletal patterns, a direct link between skeletal morphology and the prevalence of tooth fractures has not been established. This study aimed to investigate whether sagittal and vertical skeletal patterns, as determined by cephalometric analysis, are associated with the prevalence of tooth fractures in an adult orthodontic population. METHODS:A retrospective review was conducted of 1001 adult orthodontic patients with complete records, including lateral cephalometric radiographs and demographic data. Patients were classified into vertical (high, neutral, low mandibular angle) and sagittal (Angle Class I, II, III) skeletal patterns using population-specific cephalometric norms. Tooth fractures were identified through clinical records and Current Dental Terminology codes, including whether fractured teeth were extracted or retained, and whether endodontic treatment was involved. Statistical analysis included analysis of covariance, chi-square, and Fisher exact tests. RESULTS:No significant differences were observed in the prevalence of tooth fractures, either extracted or retained, across vertical or sagittal skeletal classes for the overall population. Within the Caucasian subpopulation, a significantly higher prevalence of tooth fractures was noted in ANGLE-I compared to ANGLE-II (P = .02). Similarly, previously endodontically treated teeth were more frequently extracted due to fracture in ANGLE-I and ANGLE-III compared to ANGLE-II (P = .02). No significant associations were found in the African-American, Asian, or Hispanic subpopulations. CONCLUSIONS:Craniofacial skeletal patterns may not aid in predicting tooth fracture risk in the general population. While limited associations were noted within the Caucasian subgroup, further prospective studies incorporating direct bite force measurements are warranted to clarify biomechanical contributions to tooth fractures.

Cone beam computed tomography (CBCT) is a widely-used imaging modality in dental healthcare. It is an important task to segment each 3D CBCT image, which involves labeling lesions, bones, teeth, and restorative materials on a voxel-by-voxel basis, as it aids in lesion detection, diagnosis, and treatment planning. The current clinical practice relies on manual segmentation, which is labor-intensive and demands considerable expertise. Leveraging Artificial Intelligence (AI) to fully automate the segmentation process could tremendously improve the quality and efficiency of dental healthcare. The main hurdle in this advancement is reducing AI's reliance on a large quantity of manually labeled images to train robust, accurate, and generalizable algorithms. To tackle this challenge, we propose a novel Oral-Anatomical Knowledge-informed Semi-Supervised Learning (OAK-SSL) model for 3D CBCT image segmentation and lesion detection. The uniqueness of OAK-SSL is its capability of integrating qualitative oral-anatomical knowledge of plausible lesion locations into the deep learning design. Specifically, the unique design of OAK-SSL includes three key elements, including transformation of qualitative knowledge into quantitative representation, knowledge-informed dual-task learning architecture, and knowledge-informed semi-supervised loss function. We apply OAK-SSL to a real-world dataset, focusing on segmenting CBCT images that contain small lesions. This task is inherently challenging yet holds significant clinical value as treating lesions at their early stages lead to excellent prognosis. OAK-SSL demonstrated significantly better performance than a range of existing methods. Note to Practitioners-This study tackles the challenges arising from a limited amount of labeled data due to the time-consuming manual segmentation of 3D dental cone beam computed tomography (CBCT) images. The scarcity of labeled data often impedes AI models from accurately segmenting periapical lesions. To overcome this, we introduce a novel semi-supervised learning algorithm that integrates the oral-anatomical knowledge about lesion location for 3D CBCT image segmentation. Our method effectively segments periapical lesions, including even small-sized periapical lesions, without solely relying on labeled data. The proposed method offers two significant benefits to clinicians. First, it reduces the necessity for large amounts of labeled data, particularly easing the burden of manually segmenting early-stage periapical lesions. Second, it helps reduce intra- and inter-observer disagreements and human errors by providing consistent and automated segmentation maps. These benefits not only simplify the segmentation process in dental imaging but also improve its reliability. As a result, our automated algorithm makes it easier and more trustworthy for practitioners. However, practitioners should be aware that the effectiveness of our method relies on the assumption of consistency between labeled and unlabeled data. When applying this method, it is crucial to carefully consider the characteristics of the unlabeled dataset. Significant differences in image quality, patient demographics, or acquisition parameters between labeled and unlabeled data might affect model performance.

Introduction and aims: To evaluate knowledge regarding the management of deep carious lesions and exposed pulps among undergraduate and postgraduate endodontic students from ten dental institutions across ten countries, and the impact of operator (material, antibiotic prescription) and patient-related (age, symptoms) factors on their treatment protocols. Methods: An online questionnaire was distributed to evaluate student knowledge of the management of deep caries and exposed pulp related to four clinical scenarios. Simple descriptive statistics were used to describe the data and McNemar tests were employed to identify significant differences between the scenarios. The P-value was set at 5%. Results: A total of 435 undergraduates and 139 postgraduates from ten dental schools participated in this survey. The final survey included 401 responses from undergraduates and 127 from postgraduates for statistical analysis. When symptoms were present, the majority of undergraduate and postgraduate students preferred non-selective (complete) caries removal over selective (partial) caries removal in young patients. The majority of postgraduates preferred partial pulpotomy in younger patients and pulpectomy and root canal treatment (RCT) in older patients. The majority of undergraduates preferred pulpectomy and RCT in both young/old patients when symptoms were present. The majority of undergraduates and postgraduates opted for mineral trioxide aggregate and Biodentine, respectively, when treating the exposed pulp. Systemic antibiotics were not recommended by both undergraduates and postgraduates, regardless of the patient's age and symptoms. Conclusion: Among the scenarios surveyed, the majority of undergraduates and postgraduates preferred: a) pulpectomy and RCT for older patients in the presence or absence of symptoms; b) hydraulic calcium silicate cements as pulp capping material; and c) did not recommend systemic antibiotics. Clinical relevance: The majority of students choose non-selective (complete) caries removal in all cases and if the pulp is exposed, the use of hydraulic calcium silicate cements iwas the preferred material. Systemic antibiotics are considered unnecessary, irrespective of the patient's age and symptoms.

Statement of problem. Long-term restoration success depends on a precision marginal fit to prevent marginal leakage and caries. The successful fit of a computer-aided design and computer-aided manufactured (CAD-CAM) crown may be affected by different workflow variables, including preparation, scanning, crown design, milling, sintering, and cementation. Discrepancies in any of these steps may result in poor marginal and internal fit. Evidence suggests that tooth preparation may be the most important step in the workflow for a successful outcome. Compared with the traditional means of crown preparation using the naked eye or loupes, the dental operating microscope provides higher magnification and more direct illumination. However, the impact of high magnification during preparation on the marginal quality of CAD-CAM crowns is unclear. Purpose. The purpose of this in vitro study was to compare marginal fits of CAD-CAM crowns fabricated after initial preparation with loupes and subsequent preparation refinement with either loupes or a microscope. The null hypothesis was that no significant difference would be found in the marginal gap between the preparations with loupes and those with a microscope. Material and methods. Mounted extracted molars (N=18) received initial crown preparations with a coarse grit, rounded shoulder, diamond rotary instrument with loupes of x3.0 magnification. The teeth were then randomly divided into 2 groups and refined for an additional 2 minutes with fine grit, rounded shoulder, diamond rotary instruments with either loupes (LOUP) or a microscope up to x10.0 magnification (DOM). The prepared teeth were scanned with an intraoral scanner to fabricate zirconia-reinforced lithium silicate crowns manufactured with a 4-axis milling machine, sintered in a dental furnace in accordance with the manufacturer's instructions, and cemented with self-adhesive resin cement. All teeth with crowns were mounted and scanned with a microcomputed tomography (mu CT) system at 21-mu m nominal voxel size. The resulting Digital Imaging and Communications in Medicine (DICOM) images were imported into a semiautomatic segmentation software program. Marginal and absolute gaps were measured at 24 consistent circumferential points per specimen. Absolute gaps were labeled, and the total volume was calculated. Paired and unpaired t tests were used for statistical analysis (alpha=.05). Results. The mean marginal gap was 145.0 +/- 259.6 mu m for LOUP and 35.6 +/- 110.6 mu m for DOM, with a statistically significant difference (P<.001). The mean gap volume for LOUP was 0.975 +/- 0.811 mm(3), and 0.250 +/- 0.477 mm(3) for DOM, also statistically significantly different (P=.023). A significant difference was found between the absolute and marginal gaps for LOUP (P=.007), but for DOM, the difference was not significant (P=.063). Conclusions. This study demonstrated that the higher magnification used during tooth preparation played a significant role in the size of marginal gaps present around CAD-CAM crowns. Crown preparations finished by using fine grit diamond rotary instruments with a microscope at higher magnification than loupes resulted in a more precise marginal fit with smaller gaps.

AimsThe future dental and endodontic education must adapt to the current digitalized healthcare system in a hyper-connected world. The purpose of this scoping review was to investigate the ways an endodontic education curriculum could benefit from the implementation of artificial intelligence (AI) and overcome the limitations of this technology in the delivery of healthcare to patients.MethodsAn electronic search was carried out up to December 2023 using MEDLINE, Web of Science, Cochrane Library, and a manual search of reference literature. Grey literature, ongoing clinical trials were also searched using ClinicalTrials.gov.ResultsThe search identified 251 records, of which 35 were deemed relevant to AI and Endodontic education. Areas in which AI might aid students with their didactic and clinical endodontic education were identified as follows: 1) radiographic interpretation; 2) differential diagnosis; 3) treatment planning and decision-making; 4) case difficulty assessment; 5) preclinical training; 6) advanced clinical simulation and case-based training, 7) real-time clinical guidance; 8) autonomous systems and robotics; 9) progress evaluation and personalized education; 10) calibration and standardization.ConclusionsAI in endodontic education will support clinical and didactic teaching through individualized feedback; enhanced, augmented, and virtually generated training aids; automated detection and diagnosis; treatment planning and decision support; and AI-based student progress evaluation, and personalized education. Its implementation will inarguably change the current concept of teaching Endodontics. Dental educators would benefit from introducing AI in clinical and didactic pedagogy; however, they must be aware of AI’s limitations and challenges to overcome.

OBJECTIVE:The aim was to compare the 'reverse sandwich restoration' to resin composite restorations re- garding marginal adaptation, fracture resistance, favourable/unfavourable fractures in the management of external cervical resorption. METHODS:Forty-eight extracted maxillary central incisors were selected and endodontically treated. Cervical regions of the labial root surfaces received simulated resorptive defects and were restored as three randomly allocated groups: Reverse Sandwich Restoration (resin composite + resin-modified glass ionomer) (RSR); resin composite restoration (COMP), and no restoration (NR). Each group was further divided into two subgroups (n=8 each): Thermomechanical Aging (TA) (equivalent to one year) and No Aging (NA). Marginal adaptation was scored by scanning electron microscopy. Fracture resistance was tested using a universal testing machine. Favourable versus unfavourable fractures were classified based on fracture extent. RESULTS:TA decreased the marginal adaptation for both RSR and COMP. Mean fracture resistance per groups were: RSR-NA 1522.4+-94.9N, RSR-TA 939.6+-72.9N, COMP-NA 1197.6+-95.7N, COMP-TA 870.4+-86.3N, NR-NA 1057.1+-88.1N, and NR-TA 836.6+-81.9N, respectively. Fracture resistance was the highest for RSR- NA compared to all other groups (p<0.05). TA decreased the fracture resistance in all groups (p<0.05), there was no significant difference between RSR and COMP regarding fracture resistance and favourable/ unfavourable fractures (p>0.05). CONCLUSION:RSR provided comparable results to resin composite fillings to restore artificial cervical defects pertaining to marginal adaptation, fracture resistance, and favourable versus unfavourable fractures. RSR is preferable due to its inherent biocompatibility to the periodontium. (EEJ-2023-04-050).

Welcome to the latest issue of the JOE. Here, we share some of our favorite articles that are published in this issue of the Journal. We hope you look forward to reading these and other articles in the JOE.