Purpose: To evaluate whether Candida albicans adhesion differs between denture base resins fabricated using computer-aided design/computer-aided manufacturing (CAD/CAM; milled or 3D-printed) systems and those fabricated using conventional heat-polymerized resin (HPAR).

Study selection: This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and PRISMA-Network Meta-Analyses (PRISMA-NMA) guidelines. The research question was, “For the fabrication of denture bases, are resins from the CAD/CAM system more prone to Candida albicans adhesion compared with conventional resins?” Searches were conducted in PubMed/MEDLINE, Scopus, Web of Science, Lilacs, and Embase, as well as through manual searches up to May 2025, with no language or date restrictions. The risk of bias was assessed using RoBDEMAT software. Surface roughness and contact angle were assessed using NMA.

Results: The search identified 2960 records, of which 16 in vitro studies met the eligibility criteria. In four studies, HPAR was compared with milled resins; in ten, HPAR was compared with milled and 3D-printed resins, and only 3D-printed resins were evaluated in two. Overall, the milled resins exhibited lower C. albicans adhesion, whereas the 3D-printed resins exhibited higher adhesion in five studies. NMA did not significantly differ in surface roughness or contact angle between the resin types.

Conclusions: Within the limitations of the in vitro evidence, CAD/CAM milled resins were less prone to Candida albicans adhesion than conventional and 3D-printed resins. NMA indicated that the resin type did not significantly influence the surface roughness or contact angle. Further clinical studies are required to validate these findings.

Purpose: This study aimed to develop an artificial intelligence (AI) system using a convolutional neural network (CNN) to design major connectors for removable partial denture (RPD) frameworks. Unlike rule-based or case-matching expert systems, the proposed model automatically generated RPD designs from comprehensive oral data using gradient-weighted class activation mapping (Grad-CAM) enhancing explainability.

Methods: Data were obtained from 1000 RPDs (457 maxillary, 543 mandibular) designed by prosthodontic specialists, including 255 dental variables from oral examinations. Ten CNN models were constructed sequentially to predict denture type (resin/metal), unilateral/bilateral design, absence/presence of a major connector, and four and two connector types for the maxilla and mandible, respectively. This sequential prediction followed clinical logic. Model performance was assessed by accuracy, sensitivity, specificity, positive and negative predictive values, and F1 score. Grad-CAM was employed to visualize input factors influencing predictions.

Results: The CNN models predicted major connector types with high accuracy (maxilla: palatal bar/strap, 93.0%; palatal plate, 87.1%; horseshoe bar, 89.5%; and mandible: lingual bar or plate, 87.3%). Heatmaps highlighted clinically relevant features including periodontal condition, dentition defects, and occlusal relationships as key determinants.

Conclusions: This study demonstrated the feasibility of an AI-based system for predicting RPD major connector types with acceptable performance. The system is expected to provide valuable support to dentists by minimizing the variability owing to differences in clinical skills and experience. Grad-CAM visualization enhances interpretability, enabling both clinicians and patients to better understand the reasoning behind the RPD design process. Ultimately, this approach could improve consistency, efficiency, and training in prosthodontics.

Purpose: This study retrospectively investigated the clinical outcomes of computer-aided design/computer-aided manufacturing (CAD-CAM) resin composite crowns for molars and analyzed the morphological factors contributing to crown failure using three-dimensional (3D) digital data.

Methods: The clinical outcomes of 117 crowns in 101 patients treated at Osaka University Dental Hospital were analyzed. 3D digital data were evaluated to identify factors influencing crown debonding, focusing on luting methods, abutment morphology, and crown parameters. Survival analyses, multivariate analyses, and regression modeling were performed.

Results: During an observation period of up to 1281 days, the cumulative success and survival rates were 83.3% and 95.5%, respectively. Debonding (12.0%) was the most frequent complication and was significantly associated with the choice of luting materials (P < 0.001). In addition, 3D analysis identified greater buccolingual taper, insufficient occlusal thickness, smaller abutment surface area, and reduced abutment height as predictors of debonding. Nonlinear regression analysis revealed significant differences in the abutment parameters between SA Luting and PANAVIA V5 at the lower percentiles (P = 0.02).

Conclusions: Combining clinical outcomes with 3D data highlights the importance of precise abutment preparation and material selection to reduce the risk of crown debonding.

Purpose: To evaluate the trueness of denture bases fabricated using digital light processing (DLP) and milling methods using three-dimensional (3D) models with varying residual ridge morphologies.

Methods: Edentulous mandibular 3D models representing a well-rounded ridge (WR), knife-edge ridge (KR), and flat ridge (FR) were designed using computer-aided design (CAD) software. Denture bases for these models were created using dental CAD software and fabricated via DLP 3D printing at build angles of 0 and 45 degrees (DLP0 and DLP45) and by milling (MIL). A total of 90 denture bases were fabricated, with 10 bases per model–method combination. These bases were digitized and compared to their original CAD data to assess the adaptation across three regions: denture border, alveolar ridge, and retromolar pad. Measurements were performed at three time points: before water storage, after 1 day of water storage, and after 7 days of water storage.

Results: The MIL bases exhibited significantly lower 3D surface deviations than the DLP0 and DLP45 bases. The KR models generally exhibited greater 3D surface deviations than the WR and FR models. Temporal changes in the denture bases were significant across almost all ridge types and manufacturing methods.

Conclusions: The trueness of digitally fabricated denture bases is influenced by the residual ridge morphology and manufacturing method. Milling demonstrated superior trueness compared to DLP. Temporal dimensional changes were observed in almost all the bases.

Purpose: This systematic review aimed to investigate and compare peri-implant soft-tissue responses to tooth-colored abutment materials frequently used in implant dentistry.

Study selection: A comprehensive electronic search was performed in three databases (MEDLINE via PubMed, Google Scholar, and Scopus) to identify relevant literature. The study-selection criteria included original research articles written in English that investigated the effects of various tooth-colored abutment materials on peri-implant soft-tissue responses.

Results: In total, 136 articles were included in this systematic review. Tooth-colored abutment materials, particularly zirconia and polyetheretherketone (PEEK), facilitated favorable soft-tissue adaptation, enhanced esthetics, and contributed to long-term implant success. Zirconia demonstrated excellent biocompatibility, enhanced cell viability and attachment, and lower inflammatory responses compared to titanium, suggesting improved soft-tissue integration and reduced biofilm-related risks. PEEK exhibited favorable mechanical properties and biocompatibility but limited cell attachment due to its hydrophobicity, indicating the need for surface modification. Titanium remains the clinical standard for integration but is associated with greater inflammation and biofilm formation than tooth-colored materials.

Conclusions: This review highlights the effects of tooth-colored abutment materials on peri-implant soft-tissue responses and emphasizes the importance of selecting appropriate materials for successful dental implants. Zirconia represents a promising biological alternative to titanium, promoting a stable soft-tissue barrier that contributes to minimizing inflammation and maintaining long-term tissue health. Conversely, while PEEK offers strong mechanical properties, it faces challenges regarding cell proliferation and matrix production, limiting its optimal biological performance. Further research will provide deeper insights into the best options for enhancing patient and esthetic outcomes.