Search Results (2,253)

Background/Objectives: Impacted maxillary canines are frequently associated with variations in maxillary arch morphology; however, the relationship between impaction position and three-dimensional arch characteristics remains unclear. This study aimed to evaluate the association between buccally and palatally impacted maxillary canines and maxillary arch morphology using CBCT and three-dimensional digital model analysis. Methods: This retrospective cross-sectional study included CBCT images and three-dimensional dental models of 86 individuals with unilateral impacted maxillary canines (mean age: 16.1 ± 0.72 years). Impacted canines were classified as buccal or palatal according to CBCT findings. Maxillary arch morphology was assessed using digital model analysis. Statistical comparisons between groups were performed using independent-samples t-tests (p < 0.05). Results: The buccally impacted group demonstrated significantly greater arch length, higher arch length-to-arch width ratios, greater mesiodistal width of the four maxillary incisors and increased tooth–arch discrepancy (p < 0.05). In contrast, intermolar width and available arch space were significantly greater in the palatally impacted group (p < 0.05). No significant differences were identified in arch width or palatal depth measurements between groups (p > 0.05). Intra-examiner reliability demonstrated excellent agreement (ICC > 0.90). Conclusions: Maxillary dental arch morphology differed according to the position of impacted maxillary canines. Buccal impaction was associated with sagittal arch elongation and increased tooth–arch discrepancy. In contrast, palatal impaction was not consistently associated with reduced transverse dental arch dimensions within the measurements evaluated in this study. These findings contribute to a better understanding of the association between impacted canine position and maxillary dental arch morphology and may assist clinicians in the morphological assessment of patients with impacted maxillary canines. Full article

Background/Objectives: Dental fear and anxiety (DFA) in children can negatively affect oral health behaviors and dental care utilization. Family-related factors, particularly parental anxiety, parenting styles, and socioeconomic characteristics, may be associated with DFA. This study aimed to investigate the association between family-related factors and DFA among children aged 6–8 years in Jakarta, Indonesia. Methods: A cross-sectional study was conducted among 294 child–parent pairs recruited from 10 primary schools using multistage cluster sampling. Children’s DFA was assessed using the Children’s Fear Survey Schedule–Dental Subscale (CFSS-DS), whereas parental dental anxiety was measured using the Modified Dental Anxiety Scale (MDAS). Sociodemographic and family-related characteristics, including parenting styles, were collected using self-administered questionnaires. Data were analyzed using chi-square tests and multivariable logistic regression. Results: Overall, 34.7% of the children were classified as having DFA. Maternal employment was significantly associated with children’s DFA, with children of formally employed mothers having higher odds of DFA (aOR = 2.01, 95% CI: 1.05–3.85; p = 0.034). Parental dental anxiety was associated with children’s DFA. Children whose fathers and mothers reported high levels of dental anxiety had 4.68-fold (95% CI: 1.64–13.33; p = 0.004) and 2.50-fold (95% CI: 1.10–5.74; p = 0.029) higher odds of experiencing DFA, respectively. Dental drilling and injections were the most frequently reported fear-provoking stimuli. The final regression model explained 13% of the variance in children’s DFA. Conclusions: Parental dental anxiety and maternal employment were significantly associated with DFA among children aged 6–8 years. Family-centered preventive strategies and early identification of at-risk children may help reduce DFA and promote positive dental experiences and oral health outcomes. Full article

Background: Artificial intelligence (AI) is increasingly being integrated into implant dentistry, where clinical decision-making depends on the interpretation of complex radiographic and patient-specific data. Although multiple applications have been proposed across diagnostic imaging, treatment planning, intraoperative support and outcome prediction, their clinical validity and real-world applicability remain incompletely defined and their use raises relevant medico-legal considerations. Methods: A narrative review was conducted through a structured search of PubMed/MEDLINE, Scopus, and Web of Science, including English-language studies published between 2010 and February 2026. Clinical and experimental studies, as well as relevant reviews addressing AI applications in implant dentistry, were included. A qualitative thematic synthesis was performed due to methodological heterogeneity. Results: AI applications are mainly concentrated in diagnostic imaging, particularly CBCT analysis, where high levels of performance are consistently reported. In treatment planning, systems support specific decision-making tasks rather than comprehensive strategies, while intraoperative applications are integrated into navigation and robotic systems to improve procedural accuracy. Predictive models for implant outcomes have been developed, although their reliability remains influenced by dataset variability and study design. Conclusions: AI currently represents a supportive tool in implant dentistry, with greater applicability in standardized tasks. Its integration into complex clinical decision-making remains limited, highlighting the need for clinically oriented validation and cautious implementation in practice. Full article

Dental fluorosis (DF) remains a global public health challenge traditionally attributed to elevated water fluoride F⁻. However, the Halo Effect and environmental factors now complicate this dose–response relationship. Following PRISMA 2020 guidelines, this systematic review identified 20 observational studies (n = 21,780) via PubMed, Scopus, and Web of Science. Inclusion logic utilized the PICOS framework, specifically selecting human studies that reported quantitative water F⁻ levels alongside environmental or dietary confounders. Quality was assessed via the Newcastle–Ottawa Scale. Synthesis revealed that in optimal fluoridated areas (0.7 mg/L), mild DF prevalence reached 15–20% in cohorts with high “Halo Effect” exposure (infant formula, processed beverages) a twofold increase over historical benchmarks. High altitude (>2000 m) and arid climates further exacerbated toxicity by altering renal clearance. These factors sustain systemic fluoride levels that inhibit protease activity (MMP-20/KLK4) and induce endoplasmic reticulum stress during enamel maturation, causing hypomineralization. Current water-centric monitoring is insufficient for modern risk assessment. A transition toward Total Daily Intake (TDI) models and context-specific standards accounting for altitude and dietary diffusion is essential to balance caries prevention with systemic safety. Full article

Chronic periapical periodontitis is a persistent inflammatory disease characterized by progressive bone destruction around the tooth apex. Manual radiographic detection of these lesions is subjective and time-consuming, highlighting the need for automated diagnostic tools. This paper presents a unified deep learning framework for joint tooth segmentation and periapical lesion detection in panoramic radiographs. Our approach employs a joint process: first, a deep learning model identifies and segments individual teeth according to standard dental numbering systems, while a second one detects periapical lesions within the tooth regions obtained from the segmentation outputs in the first stage. The framework incorporates an advanced loss function (Powerful IoU v2) to improve bounding-box regression accuracy and a spatial association mechanism to map detected lesions to specific teeth based on geometric overlap analysis. Our proposed tooth segmentation model achieves an mAP@50 of 97.7% and a mean Dice coefficient of 93.5%, while the periapical lesion detector reaches an mAP@50 of 91.9%. Furthermore, our region-of-interest approach yields a 3.49× computational speedup, averaging 0.1589 s per radiograph when compared to full-image processing. Trained exclusively on open-source datasets, this reproducible framework achieves explicit tooth-to-lesion mapping, providing an efficient and practical tool for periapical lesion screening. Full article

Background: Advances in additive manufacturing and CAD/CAM technologies have expanded the use of 3D-printed orthodontic models in digital aligner workflows. Intraoral scanners (IOS) are critical for accurately capturing attachment geometries and dental morphology during these workflows. However, comparative evidence regarding IOS accuracy in models with complex orthodontic structures remains limited. Therefore, this study aimed to compare the trueness and precision of five IOS using 3D-printed orthodontic models with attachments. Methods: In this in vitro study, thirty independent single-arch 3D-printed models (either maxillary or mandibular) with orthodontic attachments were scanned twice with each IOS. The Smart Optics Vinyl laboratory scanner served as the reference scanner. Scans were aligned and superimposed in CloudCompare, and root mean square (RMS) deviation values were calculated to evaluate accuracy. Nonparametric Kruskal–Wallis and Dunn tests were applied (α = 0.05). Results: Significant differences were found among scanners for both trueness and precision (p < 0.001). Primescan, TRIOS 3, and iTero element 5D demonstrated comparable trueness (p > 0.05) and outperformed Rapideye MI-1000 (p < 0.001). iTero element 2 plus showed slightly lower accuracy but remained clinically acceptable. Primescan achieved the highest precision, significantly exceeding iTero element 2 plus, iTero element 5D, and Rapideye MI-1000 (p < 0.01). TRIOS 3 also exhibited excellent repeatability, comparable to Primescan (p = 1.000). Conclusions: All intraoral scanners, except Rapideye MI-1000, demonstrated accuracy levels generally considered clinically acceptable for digital orthodontic and additive manufacturing workflows. Primescan, TRIOS 3, and iTero element 5D exhibited similarly high trueness, while Primescan showed the most consistent precision. The ability of these scanners to reproduce fine anatomical details may improve the reliability of 3D-printed orthodontic models and in-office aligner production workflows. Full article

Oral health is a very important issue today. This approach presents an original idea: to model the dental implant as a double-articulated buckling bar on an elastic environment. The mandibular bone is considered as the elastic environment. The buckling bar is analyzed using the Transfer-Matrix Method. The risk of buckling is higher for straight bars subjected to axial compression. Therefore, knowing the critical buckling force is very important, especially in the case of dental implants. This study, based on the Transfer-Matrix Method, was carried out in two steps. In the first step, a double-articulated buckling bar on a rigid environment is considered. The second step involves studying the same doubly articulated bar, but with the joint at the lower end resting on an elastic environment. The bone in which the implant is placed is considered as this elastic environment. The Transfer-Matrix Method is easy to implement and provides quick results for problems involving the shape optimization of structural components. This article presents a completely new idea and an original approach to buckling analysis, with applications to dental implants. This work will serve as a foundation for future research involving experimental investigations of dental implants. Full article

Objective: This in vitro study evaluated the insertion torque characteristics of the KS 3 implant compared with the TSIII implant in standardized artificial bone models representing weak bone, extraction-socket-like reduced support, and maxillary sinus simulation conditions. Materials and Methods: A comparative in vitro study was performed using three models: a weak bone model, a standardized extraction-socket-like reduced-support model, and a maxillary sinus simulation model. Maximum and final insertion torque values were obtained from torque–depth curves. Torque–depth integrals were additionally calculated as exploratory secondary parameters. Statistical analyses were performed using Welch’s t-test and two-way ANOVA where appropriate, and the results were interpreted as exploratory because of the limited sample size. Results: The KS 3 implant showed higher maximum and/or final insertion torque values than the TSIII implant in the weak bone, extraction-socket-like, and maxillary sinus simulation models. In the maxillary sinus model, the torque values showed directional differences according to implant type and residual bone height under the tested fixed undersized drilling protocols for both CAS drilling and bone compaction drilling. Torque–depth integral analysis provided additional information regarding cumulative insertion resistance. Conclusions: Within the limitations of this controlled in vitro study, the KS 3 implant showed higher insertion torque values than the TSIII implant under the tested artificial bone conditions. These findings should be interpreted as in vitro insertion torque data under the tested artificial bone and drilling conditions, not as evidence of clinical superiority. In the maxillary sinus simulation model, the observed torque differences should be interpreted as the combined effect of implant macrodesign and the fixed undersized drilling protocol, rather than as an isolated macrodesign effect. Full article

Accurate tooth segmentation plays a crucial role in computer-aided dental diagnosis and treatment planning, particularly in applications such as tooth detection, lesion localization, orthodontic analysis, and implant surgery. However, panoramic dental X-ray images often suffer from tooth adhesion, low contrast, and blurred boundaries, making precise delineation difficult and potentially compromising downstream clinical analysis. To address these challenges, we propose a boundary-aware segmentation framework, termed Boundary-Aware ResU-Net (BA-ResUNet), which is built upon a ResU-Net backbone and enhanced with Coordinate Attention (CA) and explicit boundary modeling mechanisms. Specifically, CA modules are introduced into the encoder to improve spatial representation and positional awareness. In addition, a Boundary Extraction Module (BEM) is designed to capture boundary priors from shallow and deep features, while a Boundary Injection Module (BIM) progressively incorporates these cues into the decoder through foreground enhancement and background suppression. This design enables the network to better preserve inter-tooth gaps and improve boundary delineation. Experiments on the MICCAI STS-2D dental dataset demonstrate that the proposed method achieves superior performance in terms of Dice and IoU compared with representative existing methods. Ablation and qualitative analyses further show that CA and BEM/BIM play synergistic roles in improving regional overlap and boundary localization, particularly in challenging cases involving adhesion, low contrast, and indistinct contours. These results indicate that the proposed framework provides a reliable and effective solution for panoramic tooth segmentation and has promising potential for computer-aided dental applications. Full article

Dental diseases have long been taught and treated as separate entities: cariology, operative dentistry, endodontics, and periodontology, each working within its own boundaries. However, increasing biological and clinical evidence suggests that this classified view does not fully reflect how disease progresses in the mouth. Instead, dental disease should be understood as a continuum within the interconnected tooth–pulp–periodontium complex. This review provides current evidence showing how dental caries can serve as the starting point of a process that can progress through pulpitis and apical periodontitis and eventually affect surrounding periodontal tissues. Caries is now widely known as a biofilm-driven and host-influenced condition shaped by ecological imbalance rather than specific pathogens alone. As lesions penetrate deeper into dentin, the structure becomes more permeable, permitting diffusion of microbial metabolites and signaling molecules toward the pulp. This initiates a multifaceted inflammatory reaction within the pulp tissue. At this stage, pulpitis becomes a critical turning point, where the outcome depends on microbial load, lesion activity, host response, and quality of clinical intervention. If the disease is not well controlled, it may lead to pulp necrosis, allowing infection to spread beyond the root canal and initiate periapical inflammation. Through anatomical pathways such as apical foramina and lateral canals, these processes can extend further, sometimes resembling or overlapping with periodontal disease. This overlap creates diagnostic challenges, as conventional tests may not always distinguish between conditions. A structured, pathway-based diagnostic approach is therefore essential. From a treatment perspective, this continuum model highlights early intervention, minimally invasive care, preservation of pulp vitality when possible, and maintenance of a strong coronal seal. Ultimately, stronger integration across dental disciplines can improve diagnosis, guide treatment decisions, support long-term tooth preservation, and promote unified dental education. This article presents a narrative review supported by a structured literature search and proposes a clinically actionable framework that extends established endodontic–periodontal concepts upstream to include caries initiation and restorative modulation. Full article

Background/Objectives: The trainer–doctor model (TDM) is a participatory paradigm in which the physician acts as a mentor and educator. Effective health communication and patient engagement are key determinants of treatment adherence and health outcomes. Based on this conceptual framework, the present study aimed to assess preferences for the Trainer–Doctor Model among dental practitioners and patients, examine the influence of demographic variables, and provide a preliminary psychometric evaluation of the TDM questionnaire in accordance with the COSMIN (COnsensus-based Standards for the selection of health Measurement Instruments) criteria. Methods: A cross-sectional survey was conducted in Romania between May 2023 and April 2024. The study included dental practitioners recruited during scientific dental conferences and patients recruited from a private dental practice in Alba Iulia, Romania. Eligible participants were adults aged 18 years or older who provided written informed consent and completed the data protection requirements. Individuals younger than 18 years of age or those who did not provide complete informed consent were excluded. Participants completed a 12-item Likert-type questionnaire assessing preferences toward the Trainer–Doctor Model. Results: Both groups showed high TDM preference (practitioners: 43.93 ± 5.56; patients: 44.77 ± 4.84; p = 0.195); 71–76% of responses were high-preference (≥4). Cronbach’s α with reverse-scored items was 0.752/0.651. EFA (KMO = 0.740; Bartlett’s p < 0.001) identified a 3-factor structure, explaining 51.3% of the variance. Patients scored significantly higher on items A (p = 0.002), B (p = 0.022), and F (p = 0.005). Conclusions: Both groups demonstrate a strong, consistent preference for TDM across demographics. The preliminary psychometric evaluation indicates acceptable internal consistency and structural validity; however, further validation, including Delphi-based content validation and confirmatory factor analysis, is required. Full article

The success of dental implantation depends on both mechanical stability and the host’s immune response to the implanted biomaterials. Osteoimmunology emphasizes that early immune responses at the implant-tissue interface are critical for bone healing and long-term osseointegration. The immune response primarily consists of immune cells, particularly macrophages, neutrophils, and lymphocytes, which interact with osteogenic cells through cytokine networks and signalling pathways, such as RANK/RANKL/OPG. Additionally, it modulates both bone formation and resorption. This review focuses on summarizing the mechanisms that shape the immune response around implants by dental implant materials. It describes mechanisms related to bulk composition, surface topography, and mechanical properties, and highlights macrophage polarization and the transition from inflammation to regeneration. The review discusses current immunomodulatory strategies, including bioactive surfaces, ion doping, nanopatterning, drug-releasing surfaces, and responsive materials, as well as advances enabled by additive manufacturing. The review also discusses experimental models used to study osteoimmunological interactions and the clinical significance of immune dysregulation in peri-implant diseases. The design of biomaterials based on osteoimmunology represents a shift toward immune-compatible implants that aim to improve regenerative outcomes and long-term implant success. Full article

In this study, we evaluate the diagnostic performance of a U²-Net-based artificial intelligence (AI) model for identifying the pterygomaxillary fissure on dental panoramic radiographs and investigate its potential utility as a supportive tool for preliminary anatomical landmark identification and pre-surgical screening. A total of 270 panoramic radiographs showing at least one fully visible pterygomaxillary fissure were retrospectively selected. In these anonymized images, 501 pterygomaxillary fissures were identified and manually annotated by two independent examiners using CVAT v1.7.0 labeling software. On the test dataset, the segmentation model achieved a Dice coefficient of 0.904 (95% CI: 0.876–0.930) and an Intersection over Union (IoU) of 0.846 (95% CI: 0.810–0.879). Precision and recall values were 0.921 and 0.902, respectively, yielding an F1-score of 0.911. During training, the highest validation Dice coefficient reached 0.910, with a validation IoU of 0.844 and validation accuracy of 0.998. These findings demonstrate that the proposed model shows strong performance in accurately segmenting the pterygomaxillary fissure on panoramic radiographs and may serve as a supportive tool for preliminary anatomical landmark identification during initial anatomical assessment. Full article

Objective: The current investigation aims to assess the clinical efficacy of intraoral photographic assessment in detecting dental caries of varying severity and to assess different variables, such as the type of dentition, examiner experience, and the type of imaging equipment, on evaluative clarity. Methods: This meta-analysis of the PRISMA-DTA systematic review and diagnostic test accuracy was conducted. They searched electronic databases such as PubMed, Web of Science, Scopus, Embase, and Cochrane Library from the beginning of time up to January 2025. The studies had to have evaluated intraoral photographic caries because they were required to have compared it with clinical intraoral examination and provide extractable tooth-level 2 × 2 data. Enamel (ICDAS 1 3), dentine (ICDAS 4 6), and any caries (ICDAS 1 6) were analyzed separately in a meta-analysis. A random-effects model was used to compute pooled sensitivity, specificity, diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) curves. Subgroup analysis was done on a pre-specified basis according to dentition, type of examiner, and imaging device. This study has been registered in PROSPERO with reference number 2026 CRD420261330820. Results: Twenty-three studies were retrieved through a comprehensive search and were stratified by severity into three categories. In the case of enamel caries, sensitivity was 0.65 (95% CI: 0.62–0.68), specificity was 0.95 (95% CI: 0.94–0.95), DOR was 36.74 (95% CI: 12.44–108.49), and the AUC was 0.87. In the case of dentine caries, the pooled sensitivity and specificity were 0.86 (95% CI: 0.85–0.87) and 0.96 (95% CI: 0.96–0.97), respectively, which produced the DOR of 176 (95% CI: 91.2–339.6) and the AUC of 0.94. Any caries had a pooled sensitivity of 0.81 (95% CI: 0.80–0.83), specificity of 0.97 (95% CI: 0.96–0.97), DOR of 64.04 (95% CI: 11.65–351.94), and AUC of 0.888. Subgroup analyses revealed that diagnostic accuracy was greater when the lesions were severe. Conclusions: Intraoral photographic assessment has a moderate level of accuracy in detecting enamel lesions and has a clinically acceptable level of accuracy in detecting dentine caries. The clinical efficacy increased with the severity of lesions and was consistent with high specificity at all levels of threshold. Imaging on smartphones could be a promising method for caries screening. Full article

Background/Objectives: Medication-related osteonecrosis of the jaw (MRONJ) is a serious complication in osteoporosis patients undergoing tooth extraction. This study aimed to develop and evaluate an interpretable, machine learning–derived triage score for rapid risk stratification at the initial dental visit. Methods: This retrospective study included 850 osteoporosis patients (443 MRONJ, 407 controls) in the derivation cohort and 559 independent multicenter MRONJ cases for external evaluation. A reference random forest model identified a hierarchical feature structure, which was translated into an additive integer-weighted scoring system through systematic hyperparameter optimization. Structural tipping points were identified using isotonic regression and first discrete derivative analysis. Internal performance was further characterized by sensitivity, specificity, PPV, NPV, calibration slope and intercept, the Hosmer–Lemeshow test, decision curve analysis, bootstrap optimism correction, and subgroup analyses. External evaluation assessed three-tier distribution concordance and case capture rates with non-inferiority testing. Results: The reference random forest achieved an AUC of 0.792. The final MRONJ triage score (range 0–17) incorporated six binary predictors with mutually exclusive drug route categories. The triage score preserved discriminative performance (AUC 0.772; ΔAUC = 0.020; p = 0.149). Two tipping points at scores 7 and 14 defined three risk tiers: low (0–6; 20.9%), moderate (7–13; 55.3%), and high (≥14; 83.5%). At the moderate-risk threshold (≥7), the score achieved sensitivity 90.3% (95% CI 87.2–92.7%) and specificity 45.0% (40.2–49.8%); at the high-risk threshold (≥14), specificity rose to 91.4% and PPV to 83.1%. Calibration was adequate (slope 0.994; intercept 0.0006; Hosmer–Lemeshow p = 0.381), and decision curve analysis demonstrated higher net benefit than reference strategies across all clinically relevant threshold probabilities. The bootstrap optimism-corrected AUC was 0.778, and discriminative performance remained stable across age, route, duration, and site subgroups (AUC range 0.70–0.79). In the external cohort, the case capture rate at the ≥7 threshold was non-inferior (83.4% vs. 88.0%; Δ = −4.6%; margin −10%). Conclusions: The MRONJ triage score demonstrated stable discrimination and reproducible case capture in an independent multicenter cohort. By relying on six variables obtainable at the initial dental visit, this framework may have the potential to reduce unnecessary tertiary referrals and support safer clinical decision-making, although this benefit was not directly demonstrated and requires confirmation in prospective implementation studies. Full article