Search Results (378)

Periodontal disease represents a major global concern characterized by chronic biofilm-driven inflammation, excessive oxidative stress, progressive tissue destruction, and impaired regenerative capacity. Beyond conventional antimicrobial approaches, recent progress has shifted toward host-directed and regenerative therapeutic strategies aimed at restoring both oral function and tissue homeostasis. This review consolidates current developments in nanobiotechnology-based materials that modulate immune responses, scavenge reactive oxygen species, and promote angiogenesis and osteogenesis, thereby facilitating the effective regeneration of dental and periodontal tissues. Emphasis is placed on bioresponsive hydrogels, bioactive scaffolds, and gas-releasing platforms that integrate therapeutic regulation with tissue repair. The discussion further highlights key advances in polymeric and inorganic biomaterials designed to balance antibacterial action with cellular compatibility and regenerative potential. By linking pathophysiological mechanisms with material-guided healing processes, this review provides a comprehensive perspective on emerging nanobiotechnological solutions that bridge patho-therapeutics with regenerative and clinical dentistry. Full article

Background: Digital intraoral scanning has become an essential component of modern restorative dentistry, offering enhanced accuracy, workflow efficiency, and patient comfort compared to conventional impression techniques. Despite these advantages, the accuracy of intraoral scanners (IOS) can be affected by multiple parameters, among which scanning strategy and device design are particularly influential. Purpose: This study aimed to investigate the effect of different scanning strategies on scan accuracy and precision, focusing on two widely used intraoral scanners (Medit i700 and Trios 5) in a controlled in vitro environment. Materials and Methods: A standardized digital test object was created according to ISO 20896-1 specifications to ensure uniformity and comparability. The object was printed using a high-precision 3D printer and scanned multiple times with both IOS systems, employing distinct scanning strategies under identical environmental conditions. Data analysis was performed using descriptive and comparative statistics, including Mean, Median, Mean Absolute Deviation (MAD), Root Mean Square Error (RMSE), Standard Deviation (SD), and Variance, to evaluate trueness and precision. Results: The Medit i700 consistently exhibited lower deviation values and greater precision compared with the Trios 5, reflecting higher trueness and precision. Scanning strategy influenced scan outcomes; structured, systematic scanning paths produced more stable and accurate datasets. The Trios 5 demonstrated higher variability, suggesting increased sensitivity to operator motion and scanning trajectory. Conclusions: Both the scanner type and scanning strategy substantially affect intraoral scan accuracy. The superior performance of the Medit i700 indicates greater robustness and operator-independent stability. Clinically, these results underscore the importance of standardized scanning protocols, as operator consistency may be a key determinant of digital impression accuracy and, consequently, of clinical outcomes. Full article

Objectives: The aim of this study was to present and describe a digital workflow integrating Digital Smile Design (DSD) with computer-aided design/computer-aided manufacturing (CAD/CAM) and additive manufacturing technologies for the fabrication of dental preparation guides, focusing on workflow feasibility, design reproducibility, and clinical handling. Materials and Methods: A digital workflow was implemented using intraoral scanning and Exocad DentalCAD 3.1 Elefsina software to design dental preparation guides based on digitally planned restorations. Preparation margins, insertion paths, and minimal material thickness were defined virtually. The guides were fabricated using both subtractive (PMMA milling) and additive (stereolithographic-based 3D printing) manufacturing techniques. Post-processing included chemical cleaning, support removal, additional light curing, and manual finishing. The evaluation was qualitative and descriptive, based on visual inspection, workflow performance, and guide adaptation to printed models. Results: The proposed digital workflow was associated with consistent fabrication of preparation guides and predictable transfer of the virtual design to the manufactured guides. Digital planning facilitated clear visualization of preparation margins and insertion axes, supporting controlled and minimally invasive tooth preparation. The workflow demonstrated good reproducibility and efficient communication between clinician and dental technician. No quantitative measurements or statistical analyses were performed. Conclusions: Within the limitations of this qualitative feasibility study, the integration of DSD with CAD/CAM and 3D printing technologies represents a viable digital approach for designing and fabricating dental preparation guides. The workflow shows potential for improving predictability and communication in restorative dentistry. Full article

This study proposes an automated system using deep learning-based object detection to identify implant systems, leveraging recent progress in self-supervised learning, specifically masked image modeling (MIM). We advocate for self-pre-training, emphasizing that its advantages when acquiring suitable pre-training data is challenging. The proposed Masked Deep Embedding (MDE) pre-training method, extending the masked autoencoder (MAE) transformer, significantly enhances dental implant detection performance compared to baselines. Specifically, the proposed method achieves a best detection performance of AP = 96.1, outperforming supervised ViT and MAE baselines by up to +2.9 AP. In addition, we address the absence of a comprehensive dataset for implant design, enhancing an existing dataset under dental expert supervision. This augmentation includes annotations for implant design, such as coronal, middle, and apical parts, resulting in a unique Implant Design Dataset (IDD). The contributions encompass employing self-supervised learning for limited dental radiograph data, replacing MAE’s patch reconstruction with patch embeddings, achieving substantial performance improvement in implant detection, and expanding possibilities through the labeling of implant design. This study paves the way for AI-driven solutions in implant dentistry, providing valuable tools for dentists and patients facing implant-related challenges. Full article

Background/Objectives: The clinical application of CAD/CAM restorative materials continues to evolve due to increasing demand for aesthetic, durable, and minimally invasive indirect restorations. Hybrid nanoceramics, such as Grandio disc (VOCO GmbH, Cuxhaven, Germany), are increasingly used in indirect restorative dentistry due to their favourable combination of mechanical strength, polishability, wear resistance, and bonding potential. One challenge associated with adhesive protocols for CAD/CAM materials lies in achieving durable bonds with resin cements. Extensive post-polymerization during fabrication reduces the number of unreacted monomers available for chemical interaction, thereby limiting the effectiveness of traditional adhesive strategies and necessitating specific surface conditioning approaches. This study aimed to evaluate, in a preliminary, non-inferential manner, the influence of several combined conditioning protocols on surface micromorphology, elemental composition, and descriptive SBS trends of a CAD/CAM hybrid nanoceramic. This work was designed as a preliminary pilot feasibility study. Due to the limited number of specimens (two discs per protocol, each providing two independent enamel bonding measurements), all bond strength outcomes were interpreted descriptively, without inferential statistical testing. This in vitro study investigated the effects of various surface conditioning protocols on the adhesive performance of CAD/CAM hybrid nanoceramics (Grandio disc, VOCO GmbH, Cuxhaven, Germany) to dental enamel. Hydrofluoric acid (HF) etching was performed to improve adhesion to indirect resin-based materials using two commercially available gels: 9.5% Porcelain Etchant (Bisco, Inc., Schaumburg, IL, USA) and 4.5% IPS Ceramic Etching Gel (Ivoclar Vivadent, Schaan, Liechtenstein), in combination with airborne-particle abrasion (APA), silanization, and universal adhesive application. HF may selectively dissolve the inorganic phase, while APA increases surface texture and micromechanical retention. However, existing literature reports inconsistent results regarding the optimal conditioning method for hybrid composites and nanoceramics, and the relationship between micromorphology, elemental surface changes, and adhesion remains insufficiently clarified. Methods: A total of ten composite specimens were subjected to five conditioning protocols combining airborne-particle abrasion with varying hydrofluoric acid (HF) concentrations and etching times. Bonding was performed using a dual-cure resin cement (BiFix QM) and evaluated by shear bond strength (SBS) testing. Surface morphology was examined through environmental scanning electron microscopy (ESEM), and elemental composition was analyzed via energy-dispersive X-ray spectroscopy (EDS). Results: indicated that dual treatment with HF and sandblasting showed descriptively higher SBS, with values ranging from 5.01 to 6.14 MPa, compared to 1.85 MPa in the sandblasting-only group. ESEM revealed that higher HF concentrations (10%) created more porous and irregular surfaces, while EDS indicated an increased fluorine presence trend and silicon reduction, indicating deeper chemical activation. However, extending HF exposure beyond 20 s did not further improve bonding, suggesting the importance of protocol optimization. Conclusions: The preliminary observations suggest a synergistic effect of mechanical and chemical conditioning on hybrid ceramic adhesion, but values should be interpreted qualitatively due to the pilot nature of the study. Manufacturer-recommended air abrasion alone may provide limited adhesion under high-stress conditions, although this requires confirmation in studies with larger sample sizes and ageing simulations. Future studies should address long-term durability and extend the comparison to other hybrid CAD/CAM materials and to other etching protocols. Full article

This narrative review examines contemporary applications of polymeric materials in dentistry from 2020 to 2025, spanning prosthodontics, restorative dentistry, orthodontics, endodontics, implantology, diagnostics, and emerging technologies. We searched PubMed, Scopus, Web of Science, and Embase for peer reviewed English language articles and synthesized evidence on polymer classes, processing routes, mechanical and chemical behavior, and clinical performance. Approximately 116 articles were included. Polymers remain central to clinical practice: poly methyl methacrylate (PMMA) is still widely used for dentures, high performance systems such as polyether ether ketone (PEEK) are expanding framework and implant-related indications, and resin composites and adhesives continue to evolve through nanofillers and bioactive formulations aimed at improved durability and reduced secondary caries. Thermoplastic polyurethane and copolyester systems drive clear aligner therapy, while polymer-based obturation materials and fiber-reinforced posts support endodontic rehabilitation. Additive manufacturing and computer aided design computer aided manufacturing (CAD CAM) enable customized prostheses and surgical guides, and sustainability trends are accelerating interest in biodegradable or recyclable dental polymers. Across domains, evidence remains heterogeneous and clinical translation depends on balancing strength, esthetics, biocompatibility, aging behavior, and workflow constraints. Full article

Background/Objectives: Artificial intelligence (AI) has rapidly evolved from experimental algorithms to transformative tools in clinical dentistry. Between 2020 and 2025, advances in machine learning (ML) and deep learning (DL) have reshaped diagnostic imaging, caries detection, prosthodontic design, and teledentistry, while raising new ethical and regulatory challenges. This study aimed to provide a comprehensive bibliometric and conceptual review of AI applications in dental care, highlighting research trends, thematic clusters, and future directions for equitable and responsible integration of AI technologies. In addition, the review further considers the implications of AI adoption for patient-centered care, including its potential role in supporting shared decision-making processes in oral healthcare. Methods: A comprehensive search was conducted in PubMed, Scopus and Embase for articles published between January 2020 and October 2025 using AI-related keywords in dentistry. Eligible records were analyzed using VOSviewer (v.1.6.20) to map co-occurrence networks of keywords, authors, and citations. A narrative synthesis complemented the bibliometric mapping, emphasizing conceptual and ethical dimensions of AI adoption in oral health care. Results: A total of 50 documents met the inclusion criteria. Bibliometric network visualization identified that the largest and most interconnected clusters were centered around the keywords “artificial intelligence,” “machine learning,” and “deep learning,” reflecting the technological backbone of AI-based applications in dentistry. Thematic evolution analysis indicated increasing interest in generative and multimodal AI models, explainability, and fairness in clinical deployment. Conclusions: AI has become a core driver of innovation in dentistry, enabling precision diagnostics and personalized care. However, responsible translation requires robust validation, transparency, and ethical oversight. Future research should integrate interdisciplinary approaches linking AI performance, patient outcomes, and equity in oral health. Full article

Background/Objectives: Pathological tooth loss resulting from poor oral hygiene or systemic diseases can lead to partial edentulism, affecting patients both psychologically and physically. These consequences include facial height reduction, temporomandibular dysfunction, and impaired phonetics and mastication. Immediate complete dentures are often an effective provisional solution during the transition to full edentulism; however, establishing the occlusal plane can be challenging when remaining teeth prevent a conventional wax try-in. This clinical case aims to present a qualitative clinical case study of a single patient, illustrating the use of the Broadrick Occlusal Plane Analyzer (BOPA) for the establishment of an occlusal plane in harmony with the anterior and condylar guidance. Methods: A 51-year-old male patient presented to the Department of Prosthodontics at the School of Dentistry, Autonomous University of Guadalajara, with partial edentulism, periodontal disease, and generalized Grade III tooth mobility. Immediate maxillary and mandibular complete dentures were selected as the treatment of choice. Due to the presence of remaining teeth that hindered clinical determination of the occlusal plane, the BOPA was used during the denture design process. Results: Anatomical landmarks were combined with BOPA tracing to establish an occlusal plane harmonious with anterior and condylar guidance. The center of the curve was modified to accommodate anatomic variability in anteroposterior reference points. Conclusions: The use of the Broadrick Occlusal Plane Analyzer facilitated the accurate determination of the occlusal plane for the fabrication of immediate complete dentures in a patient where clinical assessment was limited. This modification allowed the establishment of a bilateral balanced occlusal scheme, contributing to functional and acceptable provisional oral rehabilitation during postoperative alveolar healing. Full article

Preserving dental pulp vitality is a key goal in minimally invasive dentistry. Conventional materials such as calcium hydroxide and mineral trioxide aggregate (MTA) are effective but limited in bioactivity and mechanical strength. This systematic review evaluated the biological efficacy of chitosan-based nanoparticles and biomaterials for pulp capping and regeneration. Following PRISMA 2020 guidelines, electronic searches were conducted across five databases up to April 2025. Controlled in vitro and animal studies using chitosan-based nanoparticles, hydrogels, or composite scaffolds were included. Risk of bias was assessed using SYRCLE (animal) and ToxRTool (in vitro), and certainty of evidence was rated via the GRADE-Preclinical framework. Due to methodological heterogeneity, data were synthesized using direction-of-effect coding and visualized through Albatross and heatmap plots. Sixteen studies met the criteria, consistently demonstrating enhanced cell viability, mineralization, and upregulation of odontogenic and angiogenic markers (BMP-2, TGF-β₁, VEGF, DSPP) compared with MTA or calcium hydroxide. Animal models confirmed improved angiogenesis, reparative dentin formation, and pulp vitality preservation. Despite uniformly positive biological outcomes, the overall certainty was rated Low to Very Low owing to small samples and unclear randomization. Chitosan-based biomaterials show promising regenerative potential, warranting well-designed preclinical and clinical studies for translational validation. Full article

Background: Oral health problems are common among elite athletes, yet the evidence remains fragmented and inconsistent. Objectives: To estimate the prevalence and severity of oral conditions in elite athletes through a systematic review and meta-analysis. Methods: Studies published in English, Spanish, or Portuguese, with observational design, available in PubMed, Embase, CINAHL, Web of Science, Scopus, Dentistry & Oral Science Source, and LILACS databases until 5 January 2025, were included. A narrative synthesis was used to describe the studies, and a meta-analysis of prevalences was performed using a random-effects model. Study quality assessment was performed using the Joanna Briggs Institute Critical Appraisal tools. Results: A total of 10 articles were included in the systematic review and meta-analysis. The overall combined prevalence of caries was found to be 44.4% (95%CI: 33.9–55.1%), the prevalence of dental erosion was 36.5% (95%CI: 22.6–51.7%), the prevalence of gingivitis was 41.4% (95%CI: 14.7–71%), the prevalence of pericoronitis was 18.7% (95%CI: 2.3–45.4%), the prevalence of periodontitis was 10.8% (95%CI: 2.7–23.3%) and the prevalence of orofacial trauma was 15.6% (95%CI: 5.3–29.7%). High heterogeneity was observed across studies. Most studies presented limitations related to participant recruitment and sample size adequacy. Conclusions: Dental caries, gingivitis, and dental erosion are highly prevalent among elite athletes, underscoring the importance of integrating oral health assessments into sports medicine care. High heterogeneity across studies limits the precision of prevalence estimates, emphasizing the need for standardized methodologies in future research. Full article

Background: The use of computer-aided design and manufacturing (CAD/CAM) technology is rapidly expanding in modern dentistry. However, differences may exist in educational experiences and utilization competencies between dental hygiene and dental technology students. Objectives: This study aimed to assess CAD/CAM educational exposure and competencies by major and grade level. Methods: A cross-sectional online survey was conducted with 464 undergraduate students in Korea. The questionnaire covered general characteristics, CAD/CAM education experience, and competencies in scanning, CAD design, CAM operation, and post-processing. Data were analyzed using descriptive statistics, independent-samples t-tests, and correlation analysis. Results: A total of 464 valid responses were analyzed. Dental technology students reported significantly higher experience with CAD/CAM education compared with dental hygiene students (p < 0.001). Scanning, CAD design, CAM operation, and post-processing competency were all significantly higher among dental technology students and senior (3–4 year) students compared with dental hygiene and junior (1–2 year) students (p < 0.001). CAD/CAM competencies varied significantly by major and grade level. Conclusions: These findings emphasize the need to strengthen CAD/CAM training in dentistry curricula and to promote interdisciplinary digital dentistry education to prepare practice-ready graduates for a digitized clinical environment. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly embedded in CAD/CAM workflows to address persistent challenges in restorative dentistry, including unpredictable color outcomes and time-intensive crown design steps. Yet, evidence on its accuracy and efficiency remains fragmented across heterogeneous study designs and metrics. This systematic review and meta-analyses aimed to evaluate the accuracy and performance of AI for color prediction and automated crown design in CAD/CAM ceramics. Methods: A systematic review with random-effects meta-analyses. The outcomes included design time, internal fit, finish-line accuracy, color-prediction acceptability using ΔE₀₀ (AT₀₀), morphology deviation, and occlusal and proximal contacts. Results: Fifteen studies met the inclusion criteria. The meta-analyses showed that AI-equipped CAD reduced crown design time compared to conventional CAD (MD −88.7 s; 95% CI −134.5 to −42.9; I² = 72%). The internal fit showed a small advantage for AI (MD −17.1 µm; 95% CI −26.2 to −7.9; I² = 90%). For finish-line identification, the pooled mean Hausdorff distance was ~0.35 mm (95% CI 0.316–0.382; I² = 0%). For color prediction, the pooled proportion of predictions within each study’s prespecified acceptability threshold (AT₀₀) was near-universal (0.996; 95% CI 0.988–0.999; I² = 0%). Morphology and functional contacts were not pooled due to incompatible metrics and units. Narrative synthesis indicated AI performance comparable to, or favorable over, conventional/technician workflows in selected regions. Conclusions: AI for CAD/CAM dentistry shows practical promise, most clearly for design-time efficiency and with encouraging signals for internal fit, finish-line identification, and color-prediction acceptability under study thresholds. However, clinical translation should proceed cautiously. Full article

Emerging artificial intelligence (AI) and robotic surgical technologies have the potential to influence digital implant dentistry substantially. As a narrative review, and building on the foundations outlined in Part I, which described current digital tools and workflows alongside their persistent interface-related limitations, this second part examines how AI and robotics may overcome these barriers. This synthesis is based on peer-reviewed literature published between 2020 and 2025, identified through searches in PubMed, Scopus, and Web of Science. Current evidence suggests that AI-based approaches, including rule-based systems, traditional machine learning, and deep learning, may achieve expert-level performance in diagnostic imaging, multimodal data registration, virtual patient model generation, implant planning, prosthetic design, and digital smile design. These methods offer substantial improvements in efficiency, reproducibility, and accuracy while reducing reliance on manual data handling across software, datasets, and workflow interfaces. In parallel, robotic-assisted implant surgery has advanced from surgeon-guided systems to semi-autonomous and fully autonomous platforms, with the potential to provide enhanced surgical precision and reduce operator dependency compared with conventional static or dynamic navigation. Several of these technologies have already reached early stages of clinical deployment, although important challenges remain regarding interoperability, standardization, validation, and the continuing need for human oversight. Together, these innovations may enable the gradual convergence of digital technologies, real-time-assisted, unified, end-to-end implant prosthodontic workflows, and gradual automation, while acknowledging that full automation remains a longer-term prospect. By synthesizing current evidence and proof-of-concept applications, this review aims to provide clinicians with a comprehensive overview of the AI and robotics toolkit relevant to implant dentistry and to outline both the opportunities and remaining limitations of these disruptive technologies as the field progresses towards seamless, fully integrated treatment pathways. Full article

Dental caries is one of the most widespread chronic infectious diseases for humans. It results in localized destruction of dental hard tissues and has negative impacts on systemic health. Aims: This study aims to design, model, and control a novel 4-DOF dental robotic manipulator, Dentatron, specifically tailored for dental applications. The objectives were to (1) develop a compact robotic arm optimized for dental workspace constraints, (2) implement and compare three controllers—Computed Torque Control (CTC), Fuzzy Logic Control (FLC), and Neural Network Adaptive Control (NNAC), (3) evaluate tracking accuracy, transient response, and robustness in step and trajectory tasks, and (4) assess the potential of adaptive neural controllers for future clinical integration. Materials and Methods: The Dentatron system integrates a custom-designed robotic manipulator with adaptive controllers. The methodology consists of five main stages: robot modeling, control design, neural network adaptation, training, and evaluation. Simulations were performed to evaluate performance across joint tracking and Cartesian trajectory tasks using MATLAB 2022. Human-inspired trajectory design is fundamental to the Dentatron control and simulation framework to emulate the continuous curvature and minimum jerk characteristics of human upper-limb motion. The desired end-effector paths were formulated using fifth-degree polynomial trajectories that produce bell-shaped velocity profiles with gradual acceleration changes. Results: The study revealed that the Neural Network Adaptive Controller (NNAC) achieved the fastest convergence and lowest tracking error (<3 mm RMSE), consistently outperforming Fuzzy Logic Control (FLC) and Computed Torque Control (CTC). NNAC consistently provided precise joint tracking with minimal overshoot, while FLC ensured smoother but slower responses, and CTC exhibited large overshoot and persistent oscillations, requiring precise modeling to remain competitive. Conclusion: NNAC demonstrated the most robust and accurate control performance, highlighting its promise for safe, precise, and clinically adaptable robotic assistance in dentistry. Dentatron represents a step toward the development of compact dental robots capable of enhancing the precision and efficiency of future dental procedures. Full article

Background/Objectives: Artificial intelligence–based language models such as ChatGPT are increasingly used in medical communication, yet their performance compared with human clinicians remains insufficiently explored in dentistry. Because communication quality, including accuracy and empathy, is essential for patient understanding, this study aimed to compare ChatGPT’s responses with those of dentists with different levels of professional experience. Methods: Ten standardized dental patient questions were generated by the authors and answered by ChatGPT and by three dentist groups (<2 years, 2–5 years, >5 years of experience; one respondent per group, randomly selected from five eligible dentists). Subsequently, 30 dentists rated the professional quality of the responses, and 50 patients evaluated perceived empathy on 4-point scales. Group differences were analyzed using the non-parametric Friedman test with exact post hoc comparisons and Bonferroni correction. Results: ChatGPT received higher ratings than all dentist groups in both domains. Mean empathy scores were 3.23 for ChatGPT versus 1.73–2.14 for dentists, and mean quality scores were 3.50 versus 1.79–2.21 (all p < 0.001). Early-career dentists scored moderately higher than the most experienced group but consistently below ChatGPT. Due to the exploratory design and small number of respondents per experience group, these findings should be interpreted cautiously. Conclusions: ChatGPT generated responses rated as more empathetic and of higher professional quality than those of participating dentists. This suggests potential value for supporting routine, text-based dental communication. However, limitations such as lack of genuine empathy, data privacy concerns, and clinical responsibility must be considered. Larger studies are needed to validate these results Full article