Search Results (205)

Long-term aesthetic success in dentistry largely depends on the color stability of restorative materials. This study investigated the color changes (ΔE₀₀) of resin nanoceramic and lithium disilicate ceramic restorative materials used in computer-aided design and computer-aided manufacturing (CAD/CAM) systems following beverage immersion and after repolishing. One hundred specimens were prepared from lithium disilicate (Initial LiSi Block) and resin nanoceramic (Cerasmart), and polished. The specimens were divided into ten groups according to material and beverage type (n = 10) and immersed in distilled water, cola, tea, coffee, and turnip juice at 37 °C for 3 months. Color values were recorded at baseline, 1 week, 1 month, and 3 months, and after repolishing. ∆E₀₀ values were calculated using the CIEDE2000 color difference formula. Data were analyzed using three-way repeated measures ANOVA and post hoc Tukey and Bonferroni tests (α = 0.05). Material type, beverage type, and immersion time significantly affected color stability (p < 0.05). The highest ∆E₀₀ observed in the resin nanoceramic–tea group at 3 months (ΔE₀₀ = 11.39 ± 1.76). Lithium disilicate demonstrated better color stability. After repolishing, all ΔE₀₀ values were below the clinically acceptable limit (ΔE₀₀ ≤ 1.8). Repolishing may help maintain the long-term aesthetic success of dental restorations in the oral environment. Full article

The aim of this study was to evaluate the influence of prosthetic substrate type, resin cement shade, and opaquer liner application on the translucency and color matching of translucent zirconia- and lithium-based ceramics. Four A2-shade zirconia materials (Katana HTML Plus, STML, UTML, and YML), with and without an opaquer liner, lithium disilicate ceramics (Amber Mill LT and HT), and zirconia-reinforced lithium silicate (Celtra Duo) were investigated. Monolithic crowns and standardized rectangular specimens were fabricated using CAD/CAM technology and cemented with neutral, warm-shade, and opaque try-in pastes onto A2-shade composite resin and cobalt–chromium substrates. Color measurements were performed using a digital colorimeter based on the CIE L*a*b* system. Translucency parameters (TPs) and color differences (ΔE) relative to the A2 reference shade were calculated. Lithium-based ceramics exhibited significantly higher translucency than zirconia materials. Application of the opaquer liner on intaglio surface of crowns reduced their translucency. On A2-shade substrates, translucent zirconia luted with neutral or warm-shade paste demonstrated the most favorable color compatibility. In contrast, opaque try-in paste resulted in clinically unacceptable color deviations and loss of optical depth. On metallic substrates, most materials exhibited pronounced gray discoloration and substantial color mismatch, particularly lithium disilicate ceramics. These findings indicate that ceramic type, substrate color, opaquer liner application, and resin cement shade significantly influence the optical performance and final color outcome of all-ceramic restorations. Full article

The present study demonstrates that the corrosion behavior of dental cobalt–chromium (Co–Cr) alloys is strongly influenced by the interaction between microstructure, manufacturing technique, and oral chemical environment. A comparative investigation was conducted on Co–Cr specimens fabricated using four technological routes: conventional casting, CAD/CAM machining, Selective Laser Melting (SLM), and Direct Metal Laser Sintering (DMLS). The study included microstructural characterization, evaluation of generalized corrosion behavior using the rotating electrode technique, assessment of localized crevice corrosion, and quantitative analysis of the release of twenty metallic cations. Extraction tests were performed for 168 h in two media simulating aggressive oral environments: 0.07 N HCl (acidic medium) and a fluoride-containing electrolyte (0.1% NaF + 0.1% KF). Electrochemical measurements were recorded in the current density range of 10⁻¹⁰ to 10⁻⁷ A/cm², while released cation concentrations were quantified at the µg/L level. All alloys exhibited very low corrosion current densities (icorr in the 10⁻⁸ to 10⁻⁹ A·cm⁻² range), confirming overall good corrosion resistance. Among all manufacturing routes, CAD/CAM specimens demonstrated the highest electrochemical performance, with a wide passivity domain extending up to approximately 740 mV/SCE. A statistical interaction analysis between extraction media and manufacturing techniques was performed using the non-parametric Mann–Whitney (MW) U test. Among the analyzed elements, only chromium showed a statistically significant difference between media (p < 0.05), with an approximately 25-fold-higher release in acidic conditions compared with the fluoride medium, confirming the predominant role of proton-induced destabilization of the protective Cr₂O₃ passive film. In contrast, fluoride-containing media induced selective release of elements such as Cu (3× higher), W (2.5× higher), and Mo (1.4× higher), associated with complexation phenomena. The manufacturing route significantly influences corrosion behavior. Although additive manufacturing technologies (SLM/DMLS) enable highly accurate and customized prosthetic designs, rapid solidification and microstructural heterogeneities may increase susceptibility to localized corrosion compared with more homogeneous CAD/CAM materials. Clinically, these findings suggest that future restorative strategies should incorporate corrosion-aware material selection within digital workflows. As digital dentistry evolves, predictive models integrating patient-specific oral conditions may assist clinicians in selecting the most appropriate material system for long-term performance. In conclusion, the long-term success of dental Co–Cr prosthetic devices depends not only on mechanical strength and precision of fit, but also on sustained electrochemical stability in the complex oral environment. Full article

Background and Objectives: Advances in digital dentistry, particularly CAD-CAM, have improved the efficiency and precision of crown design and fabrication. Recently, artificial intelligence (AI)-integrated CAD-CAM systems have enabled automated tooth morphology generation, margin detection, and occlusal analysis, enhancing consistency and accuracy. This systematic review evaluates the accuracy of AI-assisted crown design compared with conventional and CAD-CAM workflows. Materials and Methods: A systematic search was conducted across PubMed/MEDLINE, Scopus, Web of Science, Cochrane, and LILACS for studies published between January 2010 and December 2025 that assessed the marginal fit, internal adaptation, and occlusal contact accuracy of single crowns. Screening, full-text assessment, and data extraction followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Methodological quality and risk of bias were evaluated using the Modified CONSORT checklist for in vitro studies and the Joanna Briggs Institute tools for clinical studies. Results: Of 887 records identified, 12 studies met the inclusion criteria. Nine studies showed a moderate risk of bias, two moderate-to-high, and one low-to-moderate. AI-assisted crown design demonstrated clinically acceptable internal fit and marginal adaptation, comparable or superior to CAD-CAM systems. Occlusal contact accuracy was generally comparable to CAD-CAM and technician-designed crowns, though variability was observed across AI models. Conclusions: AI-assisted crown design provides a reliable fit and marginal adaptation, with occlusal accuracy approaching conventional CAD-CAM and technician workflows. While not a replacement for clinical expertise, AI serves as a valuable adjunct, enhancing reproducibility, precision, and overall quality in restorative dentistry. Further standardized clinical studies are needed to validate long-term outcomes and optimize occlusal performance. Full article

Background: The clinical translation of natural nanomaterials and smart delivery systems in regenerative dentistry relies heavily on practitioner acceptance; however, end-user perspectives remain under-investigated. Objective: This study evaluated dental practitioners’ self-reported knowledge, attitudes, and perceived barriers regarding these innovations. The questionnaire examined stated intention and self-reported willingness, not clinical behaviour. Methods: A cross-sectional survey using convenience sampling was conducted among 713 Romanian dentists. Statistical analysis included Chi-square tests and odds ratio estimation to identify factors associated with willingness to adopt nano-formulations. Results: Self-reported familiarity was moderate, with only 19.1% of respondents describing themselves as “very familiar” with nanodentistry; nevertheless, 77.3% believed natural nano-formulations could match synthetic efficacy. High costs (36.5%), lack of long-term evidence (35.9%), and staining concerns (46.0%) were identified as primary perceived barriers. Experience with digital technologies (CAD/CAM) was significantly associated with a positive attitude toward adoption (OR = 2.05, 95% CI: 1.41–2.98, p < 0.001). Conclusions: Respondents demonstrated a generally positive attitude toward integrating bioactive nanomaterials, though widespread adoption is currently limited by economic, educational, and evidentiary gaps. Future strategies should focus on establishing clear clinical protocols, providing robust long-term safety data, and improving the aesthetic stability of natural biomaterials. Full article

Background/Objectives: Digital technologies and generative artificial intelligence (AI) are increasingly used in undergraduate dental education, yet international variations in adoption and governance remain insufficiently described. This study aimed to characterise cross-national patterns of educational software use, perceived importance for curriculum delivery, and institutional readiness for AI governance. Methods: A cross-sectional online survey of educators and academic administrators involved in undergraduate dental education captured institutional software use across teaching delivery, learning management, assessment, clinical record systems, imaging, simulation, digital workflows, and generative AI. Results: A total of 97 respondents from 38 countries completed the survey, with most institutions delivering both undergraduate and postgraduate dental education (66.0%). Videoconferencing platforms were widely adopted. LMS provision varied, with Google Classroom, Moodle, and Blackboard most frequently reported. Paper-based clinical records remained in use in 32% of institutions. Among digital PMS/EDR platforms, axiUm, Salud/Titanium, and Carestream Dental were the most prevalent. Adoption of simulation software, CAD/CAM systems, and 3D printing was inconsistent. LMS and videoconferencing were most often rated as essential, whereas simulation, scanners, CAD/CAM, and 3D printing were generally considered useful but not essential. Generative AI use was commonly reported, while formal institutional guidance and policies were frequently absent. Conclusions: Although digital integration in undergraduate dental education is widespread, its distribution is uneven across different regions and technology domains. The combination of rapid generative AI uptake and limited governance highlights an urgent need for institution-level guidance, staff development, and strategic investment to support responsible and equitable integration. Full article

Background/Objectives: Early and accurate detection of breast cancer is essential for reducing mortality and improving patient outcomes. However, the manual interpretation of breast ultrasound images is challenging due to image variability, noise, and inter-observer subjectivity. This study aims to address these limitations by developing an automated and interpretable computer-aided diagnosis (CAD) system. Methods: We propose an automated and interpretable computer-aided diagnosis (CAD) system that integrates ensemble transfer learning with Vision Transformer architectures. The system combines the Data-Efficient Image Transformer (Deit) and Vision Transformer (ViT) through concatenation-based feature fusion to exploit their complementary representations. Preprocessing, normalization, and targeted data augmentation enhance robustness, while Gradient-weighted Class Activation Mapping (Grad-CAM) provides visual explanations to support clinical interpretability. The proposed model is benchmarked against state-of-the-art CNNs (VGG16, ResNet50, DenseNet201) and Transformer models (ViT, DeiT, Swin, Beit) using the Breast Ultrasound Images (BUSI) dataset. Results: The ensemble achieved 96.92% accuracy and 97.10% AUC for binary classification, and 94.27% accuracy with 94.81% AUC for three-class classification. External validation on independent datasets demonstrated strong generalizability, with 87.76%/88.07% accuracy/AUC on BrEaST, 86.77%/85.90% on BUS-BRA, and 86.99%/86.99% on BUSI_WHU. Performance decreased for fine-grained BI-RADS classification—76.68%/84.59% accuracy/AUC on BUS-BRA and 68.75%/81.10% on BrEaST—reflecting the inherent complexity and subjectivity of clinical subclassification. Conclusions: The proposed Vision Transformer-based ensemble demonstrates high diagnostic accuracy, strong cross-dataset generalization, and clinically meaningful explainability. These findings highlight its potential as a reliable second-opinion CAD tool for breast cancer diagnosis, particularly in resource-limited clinical environments. Full article

The long-term esthetic performance of indirect restorations is closely related to the color stability and surface integrity of the restorative materials used. With the increasing use of CAD/CAM technologies, a wide range of ceramic- and resin-based materials have been developed for indirect restorative applications. These include feldspathic ceramics (VITA Mark II, VITA Zahnfabrik), hybrid ceramics (VITA Enamic, VITA Zahnfabrik), resin nanoceramic CAD/CAM blocks (Lava Ultimate, 3M), and indirect microhybrid resin composites (GC Posterior, GC Corporation). However, these materials are continuously exposed to chemical and mechanical challenges in the oral environment, such as staining from beverages and daily toothbrushing, which may compromise their optical and surface properties over time. The purpose of this in vitro study was to evaluate and compare the color change (ΔE) and surface roughness (Ra) of these materials after repeated coffee immersion and simulated toothbrushing. A total of 240 disk-shaped specimens were fabricated and subjected to three aging cycles consisting of storage in coffee or distilled water, followed by simulated toothbrushing with or without toothpaste. The color parameters were measured using a spectrophotometer according to the CIE Lab* system, surface roughness was assessed using a contact profilometer, and surface topography was qualitatively analyzed by atomic force microscopy. The results demonstrated that coffee immersion significantly increased both color change and surface roughness for all tested materials, with more pronounced effects observed in resin-based materials. Ceramic-based CAD/CAM materials (VITA Mark II and VITA Enamic) showed greater resistance to discoloration and surface degradation, whereas the resin nanoceramic material (Lava Ultimate) and the microhybrid resin composite (GC Posterior) exhibited clinically perceptible color changes and higher roughness values, particularly after toothbrushing with toothpaste. Full article

Background: Durable bonding to zirconia remains difficult because its chemically inert surface resists acid etching. Additive manufacturing (AM) enables controlled surface morphology, which may enhance micromechanical retention without additional treatments. Methods: Zirconia specimens with three AM-derived surface designs—(1) concave–convex hemispherical patterns, (2) concave hemispherical patterns, and (3) as-printed surfaces—were fabricated using a slurry-based 3D printing system and sintered at 1500 °C. Zirconia specimens fabricated by subtractive manufacturing using CAD/CAM systems, polished with 15 µm diamond lapping film and with or without subsequent alumina sandblasting, served as controls. Surface morphology was analyzed by FE-SEM, and shear bond strength (SBS) was tested after cementation with a resin-based luting agent. Results: SEM revealed regular layered textures and designed hemispherical structures (~300 µm) in AM specimens, along with step-like irregularities (~40 µm) at layer boundaries. The concave–convex AM group showed significantly higher SBS than both sandblasted and polished subtractive-manufactured zirconia (p < 0.05). Vertically printed specimens demonstrated greater bonding strength than those printed parallel to the bonding surface, indicating that build orientation affects resin infiltration and interlocking. Conclusion: AM-derived zirconia surfaces can provide superior and reproducible micromechanical retention compared with conventional treatments. Further optimization of printing parameters and evaluation of long-term durability are needed for clinical application. Full article

The purpose of this paper is to examine the application and development of CAD/CAM technologies in the modern metal cutting industry, with a focus on their role in increasing production accuracy, efficiency, and sustainability. The study presents an industrial case of laser cutting of AISI 304 stainless-steel sheets, in which two approaches are compared under identical material and technological parameters: conventional manual nesting and automatic nesting based on algorithms implemented in a CAD/CAM environment. The methodology evaluates both layouts using clear technical and economic indicators, including number of parts per sheet, material utilization, cutting time, weight of scrap, and cost per sheet. For the analyzed batch, automatic nesting increases the number of parts per sheet from 44 to 76 (≈73%), reduces the unused sheet area from 61% to 39%, and shortens the cutting time from 12 to 9 min (≈25%), which leads to a reduction in material waste by about 36% and cost savings of approximately 314 EUR per sheet. As a result, the process becomes more efficient and reliable, supporting sustainable and digital manufacturing goals. The findings confirm the importance of algorithmic optimization in CAD/CAM systems for enhancing industrial competitiveness, enabling effective resource management, and facilitating the transition towards Industry 5.0. Full article

This in vitro study evaluated the shear bond strength (SBS) of four CAD/CAM (Computer aided design/Computer aided manufacturing) polymer-based indirect composites bonded to dentin and microhybrid composite substrates using two resin cements. Gradia Plus (GP), Ceramage (Ce), Tescera ATL (TA), and Lava Ultimate (LA) were fabricated into cylindrical specimens (3 × 3 mm). Dentin substrates were obtained from extracted molars, while composite substrates were prepared from Filtek Z250 (4 mm × 2 mm). Bonding was performed using either a self-adhesive resin cement (RelyX U200; RU200) or a dual-cure adhesive resin cement (RelyX Ultimate; RU), resulting in 16 experimental groups (n = 12 per group). SBS was measured using a universal testing machine (1 mm/min), and failure modes were assessed under stereomicroscopy. Bond strength was significantly higher on composite substrates than on dentin (p < 0.001), primarily due to favorable polymer–polymer compatibility and matrix interdiffusion, which improved stress accommodation at the adhesive interface. TA and Ce showed superior adhesion when combined with RU, while LA exhibited the lowest values, particularly on dentin bonded with RU200. Overall, the dual-cure adhesive system provided stronger bonding than the self-adhesive system (p < 0.05). These findings highlight the influence of substrate type, composite architecture, and cement chemistry on interfacial performance in indirect polymer-based restorations. Full article

Background: Lingual orthodontic systems have recently advanced with the introduction of fully customized CAD/CAM-based designs featuring self-ligating (SL) mechanisms. This study aimed to evaluate the three-dimensional accuracy of a customized SL lingual system in reproducing digitally planned tooth positions. Methods: A total of 280 teeth were analyzed following treatment with a fully customized self-ligating lingual system (Harmony^®, Aso International Inc., Tokyo, Japan). Digital models obtained before treatment (T0), from the setup (TS), and after treatment (T1) were superimposed using a best fit algorithm in GOM Inspect. Tooth movements were quantified across seven biomechanically relevant parameters including tip, torque, rotation, buccolingual, mesiodistal, vertical, and overall displacement. Predicted and achieved movements were compared using paired t tests and Bland–Altman analysis. Results: The fully customized SL lingual appliance achieved an overall dentition accuracy of 92.1%. Mean accuracy for linear tooth movements was 94.5% ± 2.1% in the maxilla and 93.8% ± 2.5% in the mandible. For angular movements, mean accuracy was 90.8% ± 3.4% in the maxilla and 89.3% ± 3.9% in the mandible. The highest precision was observed in anterior teeth for mesiodistal (96.2%) and buccolingual (95.8%) movements, whereas the lowest accuracy occurred in rotational movements of the posterior segments (87.1%). No statistically significant differences were found between predicted and achieved movements for most parameters (p > 0.05). Conclusions: The fully customized SL lingual orthodontic system demonstrated high accuracy in reproducing digitally planned tooth movements, particularly in the anterior segments. Although accuracy was slightly lower in the posterior regions, the overall outcomes remained mechanically and clinically acceptable across all evaluated dimensions. Full article

The restoration of endodontically treated teeth remains a clinical challenge, particularly when substantial coronal tissue loss is present. Endocrowns fabricated using CAD/CAM technologies offer a conservative and esthetic alternative to conventional post-core systems; however, their long-term performance may be influenced by age-related mechanical and thermal stresses. This study evaluated the effect of thermomechanical aging on the marginal adaptation and fracture resistance of endocrowns fabricated from three CAD/CAM materials: zirconia-reinforced lithium silicate (ZLS), polyetherether ketone (PEEK), and 3D-printed resin. Sixty extracted human molars were endodontically treated and restored with endocrowns produced from these materials (n = 20 per group) and then subdivided into aged (n = 10) and control (n = 10) subgroups. Thermomechanical aging involved 5000 thermal cycles between 5 °C and 55 °C, and 75,000 mechanical loading cycles at 50 N. Marginal gaps were examined using scanning electron microscopy, and fracture resistance was tested under axial load at a crosshead speed of 0.5 mm/min. Data were analyzed using two-way ANOVA followed by Tukey’s post hoc test (α = 0.05). Thermomechanical aging significantly increased the marginal gaps in all materials (p < 0.05). The smallest marginal discrepancies were observed in the 3D-printed resin group, while the largest occurred in the ZLS after aging, likely due to dimensional changes during crystallization. Fracture resistance decreased in ZLS (−21.2%) and 3D resin (−20.9%) after aging (p < 0.05) but was not significantly affected in PEEK (−5.4%, p = 0.092). Thermomechanical aging adversely affects marginal adaptation across all materials, whereas its impact on strength is material-dependent. PEEK demonstrated the most stable mechanical performance and may represent a promising alternative for long-term endocrown restorations. 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

This paper presents an integrated three-dimensional (3D) quality inspection system for mold manufacturing that addresses critical industrial constraints, including zero-shot generalization without retraining, complete decision traceability for regulatory compliance, and robustness under severe data shortages (<2% defect rate). Dual optical sensors (Photoneo MotionCam 3D and SICK Ruler) are integrated via affine transformation-based registration, followed by computer-aided design (CAD)-based classification using geometric feature matching to CAD specifications. Unsupervised defect detection combines density-based spatial clustering of applications with noise (DBSCAN) clustering, curvature analysis, and alpha shape boundary estimation to identify surface anomalies without labeled training data. Industrial validation on 38 product classes (3000 samples) yielded 99.00% classification accuracy and 99.12% macroscopic precision, outperforming Point-MAE (93.24%) trained under the same limited-data conditions. The CAD-based architecture enables immediate deployment via CAD reference registration, eliminating the five-day retraining cycle required for deep learning, essential for agile manufacturing. Processing time stability (0.47 s compared to 43.68 s for Point-MAE) ensures predictable production throughput. Defect detection achieved 98.00% accuracy on a synthetic validation dataset (scratches: 97.25% F1; dents: 98.15% F1). Full article