Search Results (2,354)

Tissue engineering and regenerative medicine are shifting from passive tissue replacement toward instructive platforms that regulate cellular behavior, immune responses, vascularization, and extracellular matrix remodeling. This review examines recent advances in natural, synthetic, composite, and stimuli-responsive biomaterials, biofabrication and 3D bioprinting, stem and progenitor cell therapies, extracellular vesicles and other cell-free products, immunomodulatory scaffolds, skin organoids and organ-on-a-chip systems, nanotechnology, and artificial intelligence-assisted design. Particular emphasis is placed on plastic, reconstructive, and aesthetic applications, including skin and wound repair, craniofacial bone and cartilage regeneration, peripheral nerve reconstruction, vascularization, and dental and periodontal repair. The review also considers biomodulators and skinboosters as emerging regenerative-aesthetic interventions that aim to improve dermal hydration, fibroblast activity, collagen remodeling, and skin quality rather than provide volume replacement alone. Importantly, these technologies differ substantially in translational maturity, ranging from in vitro and preclinical platforms to early clinical interventions, established clinical products, and commercially available treatments for which durable regenerative efficacy remains incompletely validated. Throughout this review, biological plausibility and preclinical efficacy are therefore distinguished from human clinical evidence, regulatory or established clinical use, and commercial availability. Progress will require standardized characterization, mechanism-linked potency assays, clinically relevant models, and outcome measures that capture functional integration, durability, safety, and aesthetic performance. Full article

Background/Objectives: Dental age estimation is important in clinical and forensic practice, particularly when skeletal indicators are unavailable or compromised. Machine-learning models often achieve lower regression errors than conventional dental methods; however, whether this translates into better classification performance at legally relevant age thresholds remains unclear. This study compared the Demirjian and Willems methods with several machine learning models for overall accuracy and threshold-specific performance at the jurisdiction-specific ages of 12 and 15 years. Methods: A total of 1384 panoramic radiographs from individuals aged 8.00–15.99 years were retrospectively evaluated. The developmental stages of the seven left mandibular permanent teeth and sex were used as model inputs. Linear Regression, Decision Tree, Random Forest, Support Vector Regression, Multilayer Perceptron, Gradient Boosting, and XGBoost were trained using cross-validation and evaluated on an internal holdout set. Performance was assessed using regression errors, age-group-specific bias, sensitivity, specificity, balanced accuracy, and likelihood ratios. Results: Machine-learning models generally produced lower errors than conventional methods. In the holdout set, the lowest mean absolute error was 0.512 years for Support Vector Regression and Gradient Boosting, followed by 0.519 years for XGBoost, compared with 0.649 and 0.654 years for the Willems and Demirjian methods. However, lower regression error did not consistently improve threshold-specific performance. At 12 years, machine learning models increased sensitivity but reduced specificity and positive likelihood ratios relative to Willems. At 15 years, Linear Regression and Random Forest produced no positive predictions, whereas the better-performing models showed results similar to Willems. Conclusions: Lower regression error does not necessarily indicate better forensic threshold-specific classification performance. Dental age-estimation models should therefore be validated using threshold-specific likelihood ratios, classification metrics, and age-group-specific bias in addition to overall prediction errors. Full article

Objectives: To assess differential exposure and differential vulnerability to frequency of unhealthy food consumption in relation to dental caries among 5-year-old children and 12-year-old adolescents in Brazil. Methods: This cross-sectional study included 836 participants, comprising 445 children and 391 adolescents from municipalities in Mato Grosso do Sul, Brazil. Dental caries experience was assessed using the decayed, missing and filled surfaces index for primary teeth among children (dmfs) and permanent teeth among adolescents (DMFS). Unhealthy food consumption was measured according to the weekly frequency of sugar-sweetened beverages and ultra-processed foods and categorized as low, moderate or high. Socioeconomic position was assessed using equivalized per capita household income and parental schooling. Differential exposure was examined by assessing socioeconomic gradients in unhealthy food consumption. Differential vulnerability was assessed using interaction terms between socioeconomic position and unhealthy food consumption in negative binomial regression models. Absolute and relative inequalities were quantified using the Slope Index of Inequality and Relative Index of Inequality. Results: Adolescents from lower-income and lower-education households showed a higher frequency of unhealthy food consumption, whereas this pattern was not clearly observed among children. Socioeconomic inequalities in dental caries were evident in both age groups. However, evidence that socioeconomic position modified the association between frequency of unhealthy food consumption and caries was stronger and more consistent among adolescents. Sensitivity analyses using caries prevalence as a binary outcome supported this pattern. Inequality measures indicated stronger relative socioeconomic gradients among adolescents than among children. Conclusions: Socioeconomic inequalities in dental caries are shaped by unequal exposure to unhealthy food consumption and unequal vulnerability to its effects, particularly during adolescence. Full article

Background and Objectives: Artificial intelligence (AI) is increasingly used in clinical dentistry, but its diagnostic accuracy for oral mucosal lesions based on clinical photographs remains insufficiently validated. This prospective observational study compared the Top-1 diagnostic accuracy of ChatGPT-4o and ChatGPT-5 in identifying normal anatomical variants and pathological oral mucosal lesions and evaluated their performance across anatomical sites. Materials and Methods: Seventy adults with either normal anatomical variants (n = 21) or pathological oral mucosal lesions (n = 49) were consecutively recruited at the Department of Dental Medicine, University Hospital of Split, Croatia. One standardized clinical photograph per patient was analyzed by ChatGPT-4o and ChatGPT-5 under image-only and image-plus-text conditions using identical prompts. The reference diagnosis was established by an oral medicine specialist, with histopathological examination (HPE) performed when clinically indicated. Diagnostic performance was assessed using Top-1 accuracy and McNemar’s test. Results: Both models showed low accuracy with image-only input, but performance improved significantly after clinical information was added (p < 0.001). Overall Top-1 accuracy increased from 19.0% to 69.0% for ChatGPT-4o and from 9.0% to 51.0% for ChatGPT-5. For normal anatomical variants, accuracy increased from 14.3% to 81.0% and from 14.3% to 76.2%, respectively. For pathological oral mucosal lesions, accuracy increased from 20.4% to 63.3% and from 6.1% to 40.8%, respectively. ChatGPT-4o showed numerically higher accuracy than ChatGPT-5, particularly for pathological oral mucosal lesions, but no statistically significant difference was found between the models in the corresponding paired comparisons. Conclusions: Diagnostic performance was limited with image-only input but improved substantially when standardized clinical information accompanied the images. The numerical differences between models, particularly for pathological oral mucosal lesions, may be clinically relevant but do not establish superiority or equivalence. Neither model can currently replace conventional clinical diagnosis, and AI should be regarded as a clinical decision-support tool for evaluating oral mucosal lesions. Full article

Background/Objectives: Oral diseases are among the most prevalent non-communicable diseases and impose a substantial public health burden across Europe. Although general total health expenditure may reflect health-system capacity, it does not specifically measure oral healthcare financing, and evidence regarding its association with population-level oral health remains limited. The objective of this study was to assess the association between total health expenditure and oral health outcomes in European countries. The primary focus was on dental caries prevalence, while a secondary exploratory analysis examined edentulism. Methods: Using publicly available international databases, an ecological, unbalanced country-level panel dataset was constructed. The main caries model included 153 country-year observations from 25 European countries during 2015–2021. An extended model incorporating practicing dentist density included 109 observations from 20 countries. Two-way fixed-effects regression models with country and year effects and country-clustered standard errors were applied. Edentulism was examined using exploratory cross-sectional OLS models for 2019. Results: In the main model, higher total health expenditure (β = −0.00305, p = 0.027) and GDP per capita (β = −0.00024, p = 0.021) were associated with lower caries prevalence. In the extended model, only GDP per capita remained statistically significant (β = −0.00028, p = 0.021), whereas total health expenditure, tertiary educational attainment, and practicing dentist density were not significantly associated with caries prevalence. No statistically significant associations were observed in the exploratory edentulism analyses. Conclusions: Total health expenditure and GDP per capita were negatively associated with caries prevalence in the main country-level model, although the expenditure association was not maintained in the restricted extended sample. These ecological findings do not establish causality and require confirmation using more complete, oral-health-specific data. Full article

Objectives: To describe BMI-for-age and transverse dental arch measurements in growing children with Marfan syndrome (MFS) and a non-Marfan pediatric comparison group. Materials and Methods: A retrospective case–control observational study included 41 children aged 6–11 years: 15 patients with MFS (7 males, 8 females; mean age 8.8 ± 1.5 years) and 26 non-Marfan controls (10 males, 16 females; mean age 8.9 ± 1.6 years). Anthropometric parameters and BMI-for-age z-scores were assessed using the WHO 2007 Growth Reference. Digital dental models were used to measure intermolar width (IMW), intercanine width (ICW), and the IMW/ICW ratio. Descriptive statistics, exploratory between-group comparisons, and Pearson correlation analyses were performed. Results: Children with MFS showed significantly lower BMI and BMI-for-age z-scores than controls, although BMI-for-age values generally remained within the normal range. Height values were consistently elevated, reflecting the characteristic growth pattern of MFS. The MFS group showed a significantly smaller mean ICW than controls, whereas the difference in IMW was not statistically significant. The IMW/ICW ratio was also evaluated to characterize transverse dental arch proportions. Pearson correlation analyses showed no significant associations between BMI-for-age z-score and IMW or ICW in either group. In controls, age was positively correlated with BMI-for-age z-score and IMW was positively correlated with ICW; these associations were not observed between BMI-for-age z-score and dental measurements in the MFS group. Conclusions: Children with MFS showed a leaner anthropometric profile and a significantly smaller mean ICW, whereas the difference in IMW was not statistically significant. These preliminary findings should be interpreted cautiously given the small sample size and exploratory nature of the study. Further longitudinal studies with larger cohorts are warranted. Full article

Dental caries remains one of the most prevalent oral health problems worldwide, yet automated detection in intraoral photographs is challenging because of variable lighting, specular reflections, saliva, restoration margins, and subtle early demineralization. We propose WaveViT-YOLO, a hybrid architecture built on a YOLOv9m backbone and integrating (i) a discrete wavelet transform (DWT) preprocessing stage, (ii) learnable WaveletAttention modules at the feature-pyramid scales, and (iii) ViT-based MultiScaleCrossAttention fusion. On the publicly available Annotated Intraoral Image Dataset (6313images; patient-level 70/15/15 split; three independent seeds), YOLOv9m is the strongest standalone YOLO model by mAP@50 (mAP@50 = $0.807\pm 0.003$; mAP@50–95 = $0.642\pm 0.004$). WaveViT-YOLO achieves the highest measured mAP@50 ($0.814\pm 0.005$; $0.007$ absolute and $+0.87$% relative), mAP@50–95 ($0.647\pm 0.004$), F1 ($0.831\pm 0.005$), and PR-AUC ($0.845$) among the evaluated models. The model contains 26.3 M parameters, a $30.8$% increase over the 20.1 M YOLOv9m baseline. Model-only inference is $27.3\pm 1.6$ ms on an NVIDIA T4 GPU, while the current CPU DWT stage adds $235.2\pm 8.0$ ms, giving approximately 262.5 ms/image end-to-end; therefore, the current pipeline is not real-time end-to-end. Using the displayed seed-averaged mAP@50 values, the isolated relative changes are $+0.62$% for DWT and $+0.37$% for either WaveletAttention or ViT fusion, whereas the full configuration reaches $+0.87$%. The paired three-seed comparison against YOLOv9m yields $t\left(2\right)=6.06$, $p=0.026$, and Cohen’s $d_z=3.50$; because $n=3$, this analysis is treated as exploratory. Small lesions (<$0.098$% image area) remain the principal limitation (recall = $0.477$). Because evaluation uses clinician-provided annotations from one retrospective dataset and no independent external or prospective validation was completed, the system is presented as a research-stage screening architecture rather than a clinically validated diagnostic tool. Full article

Background/Objectives: Third molar development is one of the most frequently used dental indicators in forensic age assessment because its maturation continues through adolescence and early adulthood. Manual staging on panoramic radiographs requires experience and may be affected by observer variability, especially in transitional developmental stages. This study evaluated the performance of segmentation-based deep learning models for automatic third molar localization and Demirjian-based developmental stage classification on panoramic radiographs. Methods: The study used a two-stage segmentation framework. In the first stage, third molar localization was performed using 737 panoramic radiographs and 2736 annotations for teeth 18, 28, 38, and 48. In the second stage, developmental stage classification was performed using 695 panoramic radiographs and 2533 annotations grouped as AB, CD, EF, and GH according to Demirjian developmental stages. A supplementary 18-year threshold segmentation analysis was added using 695 panoramic radiographs and 2573 labels divided into training, validation, and test sets at an 80:10:10 ratio. YOLO-based segmentation models were trained using AdamW optimization and learning-rate settings recorded in the training logs. Model performance was evaluated using TP, FP, FN, precision, recall, F1-score, accuracy, mAP, Dice coefficient, Jaccard index, and performance curves. For instance segmentation, true negatives were not calculated because the number of background non-objects is not finite or clinically meaningful. Results: The third molar localization model yielded TP = 256, FP = 4, and FN = 11 on the test set. The overall accuracy was 0.9446, mAP@0.5 was 0.980, mAP@0.5:0.95 was 0.875, Dice coefficient was 0.9064, and Jaccard index was 0.8696. In developmental stage classification, yolo11x-seg produced the most balanced segmentation profile, with an accuracy of 0.7272, Dice coefficient of 0.5783, and Jaccard index of 0.5529. In the supplementary 18-year threshold segmentation analysis, yolov8x-seg produced TP = 172, FP = 37, and FN = 32. Overall accuracy was 0.8152, precision was 0.8230, recall was 0.8431, F1-score was 0.8329, mAP@0.5 was 0.635, and mAP@0.5:0.95 was 0.563. Class-wise results showed better performance for the under-18 class than for the 18-years-and-older class. Conclusions: YOLO-based segmentation models can localize third molars on panoramic radiographs with high performance. Developmental stage classification is more difficult than anatomical localization because the radiographic boundaries between adjacent developmental stages are gradual rather than discrete. The added 18-year threshold analysis provides a clinically relevant age threshold experiment, but it also shows that class imbalance and weak segmentation of the 18-years-and-older class limit direct forensic use. Segmentation-based third molar analysis should therefore be interpreted as a visually auditable decision-support workflow rather than a stand-alone legal age determination tool. Full article

Background/Objectives: Basic sciences form the conceptual foundation of dentistry, yet their delivery in dental curricula varies worldwide. As demographic shifts, growing healthcare complexity and scientific advances continue to reshape the dental profession, renewed attention to how these subjects are taught has become essential. This comparative narrative review aims to examine different approaches to basic science education in dentistry, exploring variation in curricular structure, depth, timing and pedagogical strategies across diverse dental schools. Particular emphasis is placed on models of vertical, horizontal and spiral integration, the alignment of basic sciences with clinical training and the implications of instructional design for long-term knowledge retention and professional competence. Methods: PubMed and Google Scholar were searched from November 2025 to June 2026 using terms related to dental education, curriculum integration and geographic regions. Sources were selected based on predefined relevance criteria rather than through systematic screening. Published literature was supplemented by structured outreach to faculty representatives using a standardised set of questions. Of the 25 dental schools contacted, nine responded, and five were included. Results: Across the programs examined, a structure of approximately two preclinical years followed by clinical training remained the most common. Early clinical exposure appeared to strengthen student confidence and professional identity. Heavy workload and curricular overload were recurring challenges in the dental curricula examined. Conclusions: The findings suggest that although curriculum integration is widely recognised as essential, no single model appears to be universally superior. Its effectiveness likely depends on the quality of implementation and faculty coordination. However, the lack of independent evaluation studies limits the ability to draw conclusions about the long-term effectiveness of different educational approaches. The review highlights the need for independent and systematic evaluation of dental curricula. Full article

Background: Dental anxiety is a prevalent condition in pediatric oral healthcare and linked with multiple etiologic factors. Although sociodemographic factors are frequently examined, the influence of internal psychological constructs such as self-esteem remains underexplored in children. This cross-sectional study aims to evaluate the association between dental anxiety and self-esteem among school-age Turkish children. Methods: The study was conducted with 204 participants aged 7–12 years. Dental anxiety and self-esteem were assessed using Corah’s Dental Anxiety Scale and the Rosenberg Self-Esteem Scale, respectively. Group comparisons were performed with the Mann–Whitney U and Kruskal–Wallis tests, and their association was examined with Spearman’s rank correlation. Independent predictors of dental anxiety were further identified through a stepwise multiple linear regression. Results: Dental anxiety was prevalent in 24.5% of participants. No significant differences in anxiety or self-esteem were found by gender, age, or socioeconomic status (p > 0.05). A moderate negative correlation (r = −0.301, p < 0.001) was observed between self-esteem and dental anxiety. Regression analysis confirmed self-esteem as the only significant independent predictor of dental anxiety (Adjusted R² = 0.080, p < 0.001) in this model. Conclusions: Findings indicate that self-esteem is independently associated with pediatric dental anxiety, although the explained variance was modest. Whether incorporating children’s psychological profile into behavioral management improves clinical outcomes remains to be investigated. Full article

Background and Objectives: This study aimed to investigate the prevalence and severity of dental erosion using the Basic Erosive Wear Examination (BEWE) index in an adult cohort, exploring its associations with systemic health, gastrointestinal symptoms, and dietary habits. Materials and Methods: An observational cross-sectional study was conducted with 170 adult patients at a university clinic in Cluj-Napoca, Romania, between January and June 2026. Participants completed a standardized questionnaire detailing general characteristics, diet, systemic conditions, and symptoms. Dental erosion was clinically recorded via the highest BEWE score per sextant. Chairside tests evaluated unstimulated salivary pH and buffering capacity. Data were analyzed using chi-square tests and ordinal logistic regression. Results: Most patients exhibited low BEWE risk. Bivariate analyses revealed significant associations between elevated BEWE risk and advanced age, GERD, eating disorders, and carbonated beverage consumption. However, in the adjusted ordinal logistic regression model, only advanced age, GERD, digestive symptoms (difficulty swallowing, digestive burns), and specific medications (anti-asthma drugs, aspirin) remained independent predictors of increased erosive wear risk. Conclusions: Dental erosion is a multifactorial condition strongly shaped by advanced age, GERD, eating disorders, and carbonated beverage intake. A comprehensive risk-based approach incorporating medical histories, symptom screening, and dietary counseling is essential for early clinical identification and prevention. Full article

The aim of this in vitro study is to evaluate the stress distribution of different definitive restorative materials designed with different connector dimensions using finite element analysis. Two adjacent prepared maxillary molars were designed digitally. Two-unit splinted fixed dental prostheses (FDPs) were designed with 4 different triangular connector dimensions (2 × 3, 3 × 3, 3 × 4, and 4 × 4 mm (width × length)). The tested materials are Gold Metal, Base Metal Alloy, Feldspathic Porcelain, Lithium Disilicate, Zirconia, and Zirconia-Reinforced Lithium Silicate. A total of 56 two-unit splinted crowns models were designed and evaluated using finite element analysis (FEA) in Autodesk Fusion 360. FEA demonstrated a non-linear relationship between connector size and performance, with the 3 × 4 mm design exhibiting optimal stress distribution and the highest safety factor. Among different materials, the base metal alloy showed the highest safety factor across all configurations, while zirconia and lithium disilicate performed comparably under static loading. The 3 × 4 mm connector demonstrated optimal performance across all tested materials. Base metal alloy exhibited the highest safety factor among all connector dimensions. Full article

Objectives: To develop and validate a fully digital, surgeon-oriented interactive framework for final dental occlusion alignment in orthognathic patients. Methods: A digital framework integrating automatic alignment, using a two-dimensional Iterative Closest Point (2D ICP) algorithm with geometric corrections and optimization-based refinement, and an interactive graphical user interface (GUI) for standardized manual refinement were implemented in MATLAB. Validation was performed on preoperative digital models from 21 orthognathic patients. Obtained digital occlusions were compared with manually articulated physical models, considered the reference standard. Translational and rotational discrepancies were assessed before and after manual refinement. Results: Twenty-one patients were included. Manual refinement reduced the greatest translational directional bias along the Y-axis (MV: −2.26 ± 2.1 to −1.17 ± 0.92 mm) and significantly decreased the magnitude of corresponding error (MAV: 2.54 ± 1.73 to 1.26 ± 0.78 mm; p = 0.002). Error magnitude also decreased along the X-axis (1.12 ± 0.84 to 0.38 ± 0.31 mm; p = 0.002), whereas a small increase was observed along the Z-axis (0.93 to 1.21 mm; p = 0.02). Overall translational RMSE improved significantly (1.83 ± 0.91 to 1.09 ± 0.39 mm; p = 0.001). Among rotational components, yaw showed the greatest reduction in error magnitude (MAV: 2.61 ± 1.91° to 0.83 ± 0.71°; p = 0.001), while no statistically significant changes were detected for pitch (p = 0.09) or roll (p = 0.13). Rotational RMSE decreased from 2.00 ± 1.01° to 1.35 ± 0.25° (p = 0.01). Conclusions: The proposed pipeline was completed for all cases. Automatic alignment provided a reliable starting point, but standardized manual refinement remained essential. Full article

Additive manufacturing is increasingly integrated into digital prosthodontic workflows for the fabrication of dental models; however, printing-related parameters may influence the dimensional accuracy of clinically relevant regions. This in vitro study evaluated the effect of support spacing on the region-specific dimensional accuracy of masked stereolithography (MSLA)-printed dental models. A standardized typodont model representing a three-unit posterior fixed dental prosthesis preparation in the FDI 24–26 region was digitized to obtain a reference STL dataset. Three support spacing configurations were evaluated: 3 mm, 5 mm, and 7 mm (n = 8 per group). All specimens were fabricated using an MSLA printer and a photopolymer model resin at a layer thickness of 50 μm. After post-processing, the printed models were rescanned and compared with the reference dataset using three-dimensional deviation analysis. Root mean square (RMS) deviations were calculated for global, marginal, intaglio, and adjacent regions. Significant differences were observed in the global (p < 0.001), marginal (p = 0.001), and intaglio (p = 0.010) regions, whereas adjacent regions were not significantly affected (p = 0.070). Increasing support spacing was associated with greater dimensional deviations in the global and intaglio regions. Overall, the 3-mm support spacing demonstrated the most consistent dimensional performance across the evaluated regions. These findings indicate that support spacing is an important design parameter influencing the dimensional predictability of MSLA-printed dental models and highlight the value of region-specific accuracy assessment for optimizing support design in digital prosthodontic workflows. Full article

Accurate teeth segmentation in panoramic dental radiographs remains a challenging task due to high image noise, low contrast, the similarity in intensity between teeth and surrounding tissues, and blurred tooth boundaries. To address these challenges, we propose DoubleTransU-Net, a dual-stage hybrid CNN–Transformer architecture that combines progressive segmentation refinement with global contextual feature learning. The first stage generates an initial tooth segmentation, while the second stage progressively refines ambiguous tooth regions to improve boundary delineation and segmentation accuracy. In addition, Atrous Spatial Pyramid Pooling (ASPP) modules capture multi-scale contextual information, whereas squeeze-and-excitation (SE) blocks enhance discriminative feature representations through channel-wise feature recalibration. The proposed model was evaluated on two public panoramic dental X-ray datasets, UFBA-UESC (1500 images) and Tufts (1000 images), and compared against several state-of-the-art segmentation models, including U-Net, DoubleU-Net, Attention U-Net, TransUNet, and DeepLabv3+. On the UFBA-UESC dataset, DoubleTransU-Net achieved an Accuracy of 95.54%, a Dice coefficient of 93.72%, an Intersection over Union (IoU) of 88.18%, a Precision of 93.57%, and a Recall of 94.24%. On the Tufts dataset, it achieved an Accuracy of 91.83%, a Dice coefficient of 92.93%, an IoU of 86.80%, a Precision of 92.27%, and a Recall of 93.97%. These results demonstrate that DoubleTransU-Net consistently outperforms existing state-of-the-art segmentation methods while exhibiting strong robustness and generalization across different panoramic dental datasets, highlighting its effectiveness for tooth semantic segmentation in panoramic dental X-ray images. Full article