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.

Purpose: This review examined clinical and technological factors that influence implant placement accuracy in computer-assisted implant surgery (CAIS) systems.

Study selection: A systematic search of PubMed, Scopus, and Embase identified English-language studies published between January 2015 and January 2025. The eligible studies included randomized controlled trials, observational studies, systematic reviews, in vitro investigations, and case reports. Data extraction focused on the coronal, apical, and angular deviations. Methodological quality was appraised using the Oxford Centre for Evidence-Based Medicine (CEBM) framework and validated bias assessment tools.

Results: Fifty-three studies were included. Accuracy was influenced by patient-related factors (arch type, bone density, and edentulous span), surgical variables (flap design, operator experience, and guide protocol), and technological parameters (imaging modality, fiducial markers, and calibration). Static systems achieved high accuracy in dentate cases with stable guide support, but were less reliable in posterior or edentulous jaws. Dynamic navigation provided intraoperative flexibility and consistent performance across arches, although outcomes depended on calibration precision and operator learning curves. Robotic-assisted systems achieved the lowest mean deviations through trajectory control and haptic feedback, although evidence remains limited to early clinical and in vitro studies.

Conclusions: Although all CAIS systems achieve high accuracy, their performance varies according to the design of the system and clinical context. Static systems are effective when stabilization is ensured, while dynamic navigation offers adaptable accuracy across scenarios, and robotics exhibit the greatest consistency by reducing operator variability. Further multicenter randomized trials and standardized reporting are needed to strengthen the available evidence and guide clinical selection.

Purpose: This systematic review evaluated the clinical performance, physical-mechanical properties, and accuracy of removable partial denture (RPD) frameworks fabricated using three-dimensional (3D) printing technologies—specifically, selective laser sintering (SLS), selective laser melting (SLM), and direct metal laser sintering (DMLS)—compared to those produced by conventional casting or methods using a partial digital workflow.

Study selection: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines, a literature search was conducted in the PubMed, Scopus, Web of Science, and Cochrane databases in October 2024. Studies were included if they compared the fit, accuracy, mechanical and physical properties, and clinical outcomes of metal RPD frameworks made using 3D printing technologies with those produced using conventional casting or partial digital methods. The risk of bias was assessed using appropriate tools (modified CONSORT, ROB2, and ROBINS-I) based on the study design and a qualitative analysis was conducted. This study received no funding and was registered with PROSPERO (#CRD42024597225).

Results: Thirty studies were included: 12 compared 3D printing technologies with conventional casting, eight with partial digital methods, and 10 with both. Clinically, 3D-printed frameworks could improve retention and patient satisfaction. The laboratory results showed higher density, better mechanical properties (yield strength, surface roughness, and microhardness), and varied accuracy by component and method, with SLM and DMLS often outperforming conventional casting. The evidence was limited by methodological variability, a moderate risk of bias in many studies, and inconsistencies across the study designs and parameters.

Conclusions: 3D-printed RPD metal frameworks demonstrated clinical accuracy and mechanical-physical performance comparable or superior to those of conventional and partially digital methods for RPD frameworks, with ongoing advances expected to further enhance their precision and clinical applicability.

Purpose: This systematic review evaluates the effectiveness of artificial intelligence (AI) models in dental implant treatment planning, focusing on: 1) identification, detection, and segmentation of anatomical structures; 2) technical assistance during treatment planning; and 3) additional relevant applications.

Study selection: A literature search of PubMed/MEDLINE, Scopus, and Web of Science was conducted for studies published in English until July 31, 2024. The included studies explored AI applications in implant treatment planning, excluding expert opinions, guidelines, and protocols. Three reviewers independently assessed study quality using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies, resolving disagreements by consensus.

Results: Of the 28 included studies, four were high, four were medium, and 20 were low quality according to the JBI scale. Eighteen studies on anatomical segmentation have demonstrated AI models with accuracy rates ranging from 66.4% to 99.1%. Eight studies examined AI’s role in technical assistance for surgical planning, demonstrating its potential in predicting jawbone mineral density, optimizing drilling protocols, and classifying plans for maxillary sinus augmentation. One study indicated a learning curve for AI in implant planning, recommending at least 50 images for over 70% predictive accuracy. Another study reported 83% accuracy in localizing stent markers for implant sites, suggesting additional imaging planes to address a 17% miss rate and 2.8% false positives.

Conclusions: AI models exhibit potential for automating dental implant planning with high accuracy in anatomical segmentation and insightful technical assistance. However, further well-designed studies with standardized evaluation parameters are required for pragmatic integration into clinical settings.