Search Results (90)

Background and Objectives: Sjögren’s syndrome (SS) causes destructive salivary gland dysfunction with substantial oral morbidity. To synthesize practical, evidence-based approaches for early recognition, initial oral management, and timely referral to dental care. Materials and Methods: Narrative review of English-language literature from the Web of Science Core Collection and PubMed, prioritizing systematic reviews, randomized trials, and consensus guidelines. Results: Early oral signs include rapid multifocal root and cervical caries, burning sensations, and rising dental treatment needs. Unstimulated whole saliva ≤ 0.1 mL/min supports significant hypofunction and complements the 2016 ACR/EULAR criteria. Preventive care should combine dietary counseling, salivary stimulation, and topical remineralization. Adjuncts include high-fluoride toothpaste, biomimetic hydroxyapatite dentifrices, and casein phosphopeptide–amorphous calcium phosphate (CPP-ACP). However, evidence for fluoride varnish in SS remains mixed. Pharmacologic sialogogues require screening for contraindications. Conclusions: Embedding oral screening, simple salivary metrics, and a structured referral pathway into rheumatology visits can reduce preventable tooth loss and improve comfort, function, and treatment adherence. Full article

Background/Objectives: Artificial intelligence (AI) systems may enhance diagnostic accuracy in cone-beam computed tomography (CBCT) analysis. However, most validations focus on isolated tooth-level tasks rather than clinically meaningful full-mouth assessment outcomes. To evaluate the diagnostic accuracy of a commercial AI platform for detecting dental treatment features on CBCT images at both tooth and full-scan levels. Methods: In this retrospective single-center study, 147 CBCT scans (4704 tooth positions) were analyzed. Two experienced readers annotated treatment features (missing teeth, fillings, endodontic treatments, crowns, pontics, orthodontic appliances, implants), and consensus served as the reference. Anonymized datasets were processed by a cloud-based AI system (Diagnocat Inc., San Francisco, CA, USA). Diagnostic metrics—sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score—were calculated with 95% patient-clustered bootstrap confidence intervals. A “Perfect Agreement” criterion defined full-scan level success as an entirely error-free full-mouth report. Results: Tooth-level AI performance was excellent, with accuracy exceeding 99% for most categories. Sensitivity was highest for missing teeth (99.3%) and endodontic treatments (99.0%). Specificity and NPV exceeded 98.5% and 99.7%, respectively. Full-scan level Perfect Agreement was achieved in 82.3% (95% CI: 76.2–88.4%), with errors concentrated in teeth presenting multiple co-existing findings. Conclusions: The evaluated AI platform demonstrates near-perfect accuracy in detecting isolated dental features but moderate reliability in generating complete full-mouth reports. It functions best as an assistive diagnostic tool, not as an autonomous system. Full article

Background/Objectives: Despite decades of technological progress, the diagnosis of dental caries still depends largely on subjective, operator-dependent assessment, leading to inconsistent detection of early lesions and delayed intervention. Artificial intelligence (AI) has emerged as a transformative approach capable of standardizing diagnostic performance and, in some cases, surpassing human accuracy. This scoping review critically synthesizes the current evidence on AI for caries detection and examines its true translational readiness for clinical practice. Methods: A comprehensive literature search was conducted in PubMed, Scopus, and Web of Science (WoS), covering studies published from January 2019 to June 2024, in accordance with PRISMA-ScR guidelines. Eligible studies included original research evaluating the use of AI for dental caries detection, published in English or Spanish. Review articles, editorials, opinion papers, and studies unrelated to caries detection were excluded. Two reviewers independently screened, extracted, and charted data on imaging modality, sample characteristics, AI architecture, validation approach, and diagnostic performance metrics. Extracted data were summarized narratively and comparatively across studies using tabulated and graphical formats. Results: Thirty studies were included from an initial pool of 617 records. Most studies employed convolutional neural network (CNN)-based architectures and reported strong diagnostic performance, although these results come mainly from experimental settings and should be interpreted with caution. Bitewing radiography dominated the evidence base, reflecting technological maturity and greater reproducibility compared with other imaging modalities. Conclusions: Although the reported metrics are technically robust, the current evidence remains insufficient for real-world clinical adoption. Most models were trained on small, single-source datasets that do not reflect clinical diversity, and only a few underwent external or multicenter validation. Until these translational and methodological gaps are addressed, AI for caries detection should be regarded as promising yet not fully clinically reliable. By outlining these gaps and emerging opportunities, this review offers readers a concise overview of the current landscape and the key steps needed to advance AI toward meaningful clinical implementation. Full article

Background: Assessment of root development and apical closure is critical in dental disciplines, including endodontics, trauma management, and age estimation. This study aims to leverage advances in deep learning Convolutional Neural Networks (CNNs) to automatically evaluate the apical region status of permanent first molars, highlighting a digital health application of AI in dentistry. Methods: In this retrospective study, 262 Cone Beam Computed Tomography (CBCT) scans were reviewed, and 147 anonymized dental images were cropped from pseudopanoramic radiographs, including standard measurements. Tooth regions were resized to 471 × 1075 pixels and split into training (80%) and test (20%) sets. CNN performance was assessed using accuracy, precision, recall, F1-score, and receiver operating characteristic (ROC) curves with area under the curve (AUC), demonstrating AI-based image analysis in a dental context. Results: Precision, recall, and F1-scores were 0.79 for open roots and 0.81 for closed roots, with a macro average of 0.80 across all metrics. The overall accuracy and AUC were also 0.80. Conclusions: These results suggest that CNNs can be effectively used to assess apical patency from ROI images derived from pseudopanoramic radiographs. Full article

Background/Objectives: Panoramic radiographs aid diagnosis in paediatric dentistry, but errors occur. Deep learning-based artificial intelligence offers improved accuracy by reducing overlap-related and interpretive mistakes. This study aimed to develop a U-Net-based deep learning model for simultaneous tooth segmentation and agenesis detection, capable of distinguishing between primary and permanent teeth in panoramic radiographs. Methods: Publicly available panoramic radiographs, along with images collected from the archives of Burdur Mehmet Akif Ersoy University Faculty of Dentistry, were used. The dataset totalled 1697 panoramic radiographs after applying exclusion criteria for artifacts and edentulous cases. Manual segmentation was performed by two paediatric dentists and one dentomaxillofacial radiologist. The images were split into training (80%), validation (10%), and test (10%) sets. A U-Net architecture was trained to identify both primary and permanent teeth and to detect tooth agenesis. Results: Dental agenesis was detected in 14.6% of 1697 OPGs, predominantly affecting the mandibular second premolars (32.5%) and maxillary lateral incisors (27.6%). Intra- and inter-researcher intraclass correlation coefficients (ICCs) were 0.995 and 0.990, respectively (p > 0.05). On the test set, the model achieved a Dice similarity coefficient of 0.8773, precision of 0.9115, recall of 0.8974, and an F1 score of 0.9027. Validation accuracy was 96.71%, indicating reliable performance across diverse datasets. Conclusions: The proposed deep learning model automates tooth segmentation and agenesis detection for both primary and permanent dentitions in panoramic radiographs. Its high-performance metrics suggest improved accuracy and efficiency in paediatric dental diagnostics, potentially reducing clinician workload and minimizing diagnostic errors. Full article

Generative Artificial Intelligence (AI) has the potential to address the limited availability of dental radiographs for the development of Dental AI systems by creating clinically realistic synthetic dental radiographs (SDRs). Evaluation of artificially generated images requires both expert review and objective measures of fidelity. A stepwise approach was used to processing 10,000 dental radiographs. First, a single dentist screened images to determine if specific image selection criterion was met; this identified 225 images. From these, 200 images were randomly selected for training an AI image generation model. Second, 100 images were randomly selected from the previous training dataset and evaluated by four dentists; the expert review identified 57 images that met image selection criteria to refine training for two additional AI models. The three models were used to generate 500 SDRs each and the clinical realism of the SDRs was assessed through expert review. In addition, the SDRs generated by each model were objectively evaluated using quantitative metrics: Fréchet Inception Distance (FID) and Kernel Inception Distance (KID). Evaluation of the SDR by a dentist determined that expert-informed curation improved SDR realism, and refinement of model architecture produced further gains. FID and KID analysis confirmed that expert input and technical refinement improve image fidelity. The convergence of subjective and objective assessments strengthens confidence that the refined model architecture can serve as a foundation for SDR image generation, while highlighting the importance of expert-informed data curation and domain-specific evaluation metrics. Full article

Background/Objectives: The purpose of this study is twofold: first, to construct and evaluate a deep-learning model for automated age estimation from lateral cephalograms spanning early childhood to older adulthood; and second, to determine whether sex-specific training improves predictive accuracy. Methods: This retrospective study examined 600 lateral cephalograms (ages 4–63 years; 300 female, 300 male). The images were randomly divided into five cross-validation folds, stratified by sex and age. An ImageNet-pretrained DenseNet-121 was employed for age regression. Three networks were trained: mixed-sex, female-only, and male-only. Performance was evaluated using mean absolute error (MAE) and the coefficient of determination (R²). Grad-CAM heatmaps quantified the contributions of six craniofacial regions. Duplicate patients were excluded to minimize sampling bias. Results: The mixed-sex model achieved an MAE of 2.50 ± 0.27 years, an R² of 0.84 ± 0.04, the female-only model achieved an MAE of 3.04 ± 0.37 years and an R² of 0.82 ± 0.04, and the male-only model achieved an MAE of 2.29 ± 0.27 years and an R² of 0.83 ± 0.04. Grad-CAM revealed dominant activations over the frontal bone in the mixed-sex model; the occipital bone and cervical soft tissue in the female model; and the parietal bone in the male model. Conclusions: A DenseNet-121-based analysis of lateral cephalograms can provide a clinically relevant age estimation with an error margin of approximately ±2.5 years. Using male-only model slightly improves performance metrics, and careful attention to training data distribution is crucial for broad applicability. Our findings suggest a potential contribution to forensic age estimation, growth and development research, and support for unidentified deceased individuals when dental records are unavailable. Full article

Background/Objectives: Osseointegration of titanium dental implants is essential for the long-term success of prosthetic treatments. Cold atmospheric pressure plasma (CAP) has recently emerged as a promising surface modification technique aimed at enhancing early osseointegration by improving implant surface properties and exerting antimicrobial effects. This systematic review aims to critically evaluate the in vivo preclinical evidence on the effects of CAP or similar cold plasma treatments on titanium dental implant surfaces with regard to osseointegration outcomes. Methods: A systematic literature search was conducted in PubMed and Scopus databases for preclinical in vivo studies published between 2005 and 2025 investigating the effects of cold plasma on titanium dental implant surfaces. The primary outcome assessed was the bone-to-implant contact (BIC), followed by secondary outcomes including implant stability quotient (ISQ), removal torque, bone area fraction occupancy (BAFO), peri-implant bone density (PIBD), interfacial bone density (IBD), bone-implant direct weight (BDWT) and bone loss measurements via histology and micro-CT. Risk of bias was evaluated using the SYRCLE Risk of Bias tool. Results: Nine eligible studies involving 310 titanium implants in 71 animal models (dogs, pigs and mice) were included. CAP-treated implants consistently demonstrated significant improvements in early osseointegration parameters compared to controls, with statistically significant increases in BIC (up to +20%), BAFO and biomechanical fixation metrics (removal torque and ISQ). Micro-CT analyses revealed enhanced peri-implant bone density and architecture. No adverse biological events or implant failures related to plasma treatment were reported. However, heterogeneity in plasma protocols, animal species and short follow-up durations (2–12 weeks) limited comparability and long-term interpretation. Conclusions: Preclinical evidence seems to support CAP as a safe and potentially effective surface treatment for enhancing early osseointegration of titanium dental implants. Further standardized long-term studies involving functional loading and clinical trials in humans are needed to confirm clinical efficacy and optimize treatment protocols. Full article

Objective: To evaluate the performance of vision transformer (ViT)-based deep learning models in the classification of open apex on panoramic radiographs (orthopantomograms (OPGs)) and compare their diagnostic accuracy with conventional convolutional neural network (CNN) architectures. Materials and Methods: OPGs were retrospectively collected and labeled by two observers based on apex closure status. Two ViT models (Base Patch16 and Patch32) and three CNN models (ResNet50, VGG19, and EfficientNetB0) were evaluated using eight classifiers (support vector machine (SVM), random forest (RF), XGBoost, logistic regression (LR), K-nearest neighbors (KNN), naïve Bayes (NB), decision tree (DT), and multi-layer perceptron (MLP)). Performance metrics (accuracy, precision, recall, F1 score, and area under the curve (AUC)) were computed. Results: ViT Base Patch16 384 with MLP achieved the highest accuracy (0.8462 ± 0.0330) and AUC (0.914 ± 0.032). Although CNN models like EfficientNetB0 + MLP performed competitively (0.8334 ± 0.0479 accuracy), ViT models demonstrated more balanced and robust performance. Conclusions: ViT models outperformed CNNs in classifying open apex, suggesting their integration into dental radiologic decision support systems. Future studies should focus on multi-center and multimodal data to improve generalizability. Full article

Digital dentistry is undergoing rapid evolution, with three-dimensional imaging technologies increasingly integrated into routine clinical workflows. Originally developed for accurate dental arch reconstruction, modern intraoral scanners have demonstrated expanding versatility in capturing intraoral mucosal as well as perioral cutaneous structures. Concurrently, photogrammetry has emerged as a powerful method for full-face digital reconstruction, particularly valuable in orthodontic and prosthodontic treatment planning. These advances offer promising applications in forensic sciences, where high-resolution, three-dimensional documentation of anatomical details such as palatal rugae, lip prints, and bite marks can provide objective and enduring records for legal and investigative purposes. This study explores the forensic potential of two digital acquisition techniques by presenting two cadaveric cases of animal bite injuries. In the first case, an intraoral scanner (Dexis 3600) was used in an unconventional extraoral application to directly scan skin lesions. In the second case, photogrammetry was employed using a digital single-lens reflex (DSLR) camera and Agisoft Metashape, with standardized lighting and metric scale references to generate accurate 3D models. Both methods produced analyzable digital reconstructions suitable for forensic archiving. The intraoral scanner yielded dimensionally accurate models, with strong agreement with manual measurements, though limited by difficulties in capturing complex surface morphology. Photogrammetry, meanwhile, allowed for broader contextual reconstruction with high texture fidelity, albeit requiring more extensive processing and scale calibration. A notable advantage common to both techniques is the avoidance of physical contact and impression materials, which can compress and distort soft tissues, an especially relevant concern when documenting transient evidence like bite marks. These results suggest that both technologies, despite their different origins and operational workflows, can contribute meaningfully to forensic documentation of bite-related injuries. While constrained by the exploratory nature and small sample size of this study, the findings support the viability of digitized, non-destructive evidence preservation. Future perspectives may include the integration of artificial intelligence to assist with morphological matching and the establishment of digital forensic databases for pattern comparison and expert review. Full article

Background: Edentulism, or toothlessness, is a significant public health issue with profound implications for physical and systemic health, especially during pregnancy, when hormonal and behavioral changes increase the risk of oral diseases. Indigenous populations are particularly vulnerable due to socioeconomic and cultural factors that limit access to dental care. Methods: This pilot study assessed the oral microbiota of nine women, both pregnant and non-pregnant, aged 18–35 from the Salasaca indigenous community in Ecuador, using 16S rRNA gene sequencing. Samples were collected from dentin, saliva, and oral mucosa, and analyzed for alpha and beta diversity levels, taxonomic composition, and ecological metrics using the DADA2 pipeline and a canonical correspondence analysis. Results: Pregnant participants exhibited significantly lower microbial diversity compared to non-pregnant individuals, with notable differences in species richness and community structure. Dominant phyla included Bacillota, Bacteroidota, and Pseudomonadota. Prevotella sp., Neisseria sp., and Haemophilus sp. were among the prevalent genera, with the canonical correspondence analysis highlighting associations between microbial profiles and variables such as gestational status, marital status, and BMI. Conclusion: The findings suggest that pregnancy influences the oral microbiota composition, potentially predisposing women to dysbiosis and dental pathology. This study highlights the need for targeted oral health strategies during pregnancy and serves as a foundation for larger studies in underserved indigenous populations. Full article

Photoacoustic tomography (PAT), which combines optical absorption and ultrasonic detection, enables the monitoring of dehydration-driven structural changes in extracted teeth over time. In this proof-of-concept study, 2D photoacoustic images of a wisdom tooth were generated on the same scanning plane at days 0, 1, 3, 6, 10, 15, 21, and 28 post-extraction, using day 0 as the reference. Measurements were performed in forward-detection mode with a single ultrasound transducer and a 532 nm pulsed laser. For the comparative analysis of variations between images, four metrics were used: Pearson correlation coefficient, Structural Similarity Index (SSIM), Mean Squared Error (MSE), and Peak Signal-to-Noise Ratio (PSNR). Structural changes were also examined through radial intensity profiles extracted from each image. The results revealed marked differences in the central region, evidencing progressive structural and acoustic modifications within the tooth. The most significant change occurred on day 1, followed by small but consistent variations on subsequent days. These differences are associated with dehydration-induced changes in tissue density, which affect sound propagation. This study highlights the value of PAT for noninvasive monitoring of post-extraction dental changes, with implications for diagnosis, treatment guidance, and biomaterials research in dentistry. Full article

The increasing popularity of Multi/Unit abutments in dental restorations is attributed to their clinical advantages, yet little is known about their mechanical behavior, particularly in terms of fatigue performance. Background/Objectives: This study aimed to evaluate the mechanical behavior of Multi/Unit abutments with a focus on the impact of implant and prosthetic platform diameters on fatigue performance. Methods: Five dental restoration models were analyzed using Finite Element Analysis by incorporating implants of identical length and body diameter but varying implant platform size (3.5 and 4.1 mm) and prosthetic platform size (3.5, 4.1, and 5.5 mm). Mechanical stresses on critical sections of the screws were assessed under cyclic loads. Results: The results revealed that the implant platform diameter had minimal influence on the fatigue performance of the prosthetic screw, while a wider prosthetic platform significantly improved its mechanical behavior by reducing stress and allowing the use of larger screw metrics. These findings emphasize that the prosthetic platform diameter plays a crucial role in protecting the prosthetic screw, which is often the critical component in dental restorations that use Multi/Unit abutments. Conclusions: The study highlights the importance of carefully selecting platform dimensions to optimize the mechanical performance and longevity of dental restorations utilizing Multi/Unit abutments. Full article

Background: Dentistry is undergoing a digital transformation driven by emerging technologies such as Artificial Intelligence (AI), Internet of Things (IoT), Digital Twins (DTs), and Large Language Models (LLMs). These advancements offer new paradigms in clinical diagnostics, patient monitoring, treatment planning, and medical education. However, integrating these technologies also raises critical questions around security, privacy, ethics, and trust. Objective: This review aims to provide a structured synthesis of the recent literature exploring AI, IoT, DTs, and LLMs in dentistry, with a specific focus on their application domains and the associated ethical, privacy, and security concerns. Methods: A comprehensive literature search was conducted across PubMed, IEEE Xplore, and SpringerLink using a custom Boolean query string targeting publications from 2020 to 2025. Articles were screened based on defined inclusion and exclusion criteria. In total, 146 peer-reviewed articles and 18 technology platforms were selected. Each article was critically evaluated and categorized by technology domain, application type, evaluation metrics, and ethical considerations. Results: AI-based diagnostic systems and LLM-driven patient support tools were the most prominent technologies, primarily applied in image analysis, decision-making, and health communication. While numerous studies reported high performance, significant methodological gaps exist in evaluation design, sample size, and real-world validation. Ethical and privacy concerns were mentioned frequently, but were substantively addressed in only a few works. Notably, IoT and Digital Twin implementations remained largely conceptual or in pilot stages, highlighting a technology gap in dental deployment. Conclusions: The review identifies significant potential for converged intelligent dental systems but also reveals gaps in integration, security, ethical frameworks, and clinical validation. Future work must prioritize cross-disciplinary development, transparency, and regulatory alignment to realize responsible and patient-centered digital transformation in dentistry. Full article

This study investigates the effect of copper (Cu) and silver (Ag) additions on the electrochemical behavior of the Fe40Al intermetallic alloy in artificial saliva, aiming to evaluate its potential for biomedical applications such as dental implants. Alloys with varying concentrations of Ag (0.5, 1.0, and 3.0 wt%) and Cu (1.0, 3.0, and 5.0 wt%) were synthesized and exposed to a biomimetic electrolyte simulating oral conditions. Electrochemical techniques, including open circuit potential (OCP), linear polarization resistance (LPR), potentiodynamic polarization, and electrochemical impedance spectroscopy (EIS), were employed to assess corrosion performance. Results show that unmodified Fe40Al exhibits good corrosion resistance, attributed to the formation of a stable passive oxide layer. The addition of Cu, particularly at 3.0 wt%, significantly improved corrosion resistance, yielding lower corrosion current densities and higher polarization resistance and charge transfer resistance values, surpassing even 316L stainless steel in some metrics. Conversely, Ag additions led to a degradation of corrosion resistance, especially at 3.0 wt%, due to microstructural changes and the formation of metallic Ag precipitates, AgSCN, and galvanic cells, which promoted localized corrosion. EIS results revealed that Cu- and Ag-modified alloys developed less homogeneous and less protective passive layers over time, as indicated by increased double-layer capacitance (C_dl) and reduced constant phase element exponent (n_dl) values. Overall, the Fe40Al alloy shows intrinsic corrosion resistance in simulated physiological environments, and Cu additions can enhance this performance under controlled conditions. However, Ag additions negatively affect the protective behavior of the passive layer. These findings offer critical insight into the design of Fe-Al-based biomaterials for dental or biomedical applications where corrosion resistance and electrochemical stability are paramount. Full article