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.

Purpose: To evaluate the effects of various cleaning methods on the bond strength between lithium disilicate (LS₂) ceramics and resin cement under short- and long-term aging conditions using network meta-analysis (NMA) and pairwise meta-analysis (PMA).

Study selection: An electronic search of seven databases (PubMed, Scopus, Embase, Web of Science, Google Scholar, Defense Technical Information Center, and LILACS) with a manual search of the reference lists was conducted for articles published up to March 10, 2025. Eligible studies included those that assessed the bond strength of LS₂, either uncontaminated or contaminated with human saliva, fit-checking materials, try-in paste, or their combinations, using different cleaning methods. Risk of bias was evaluated using the RoBDEMAT tool. A frequentist multivariable random-effects model was used to calculate the effect sizes.

Results: Fifteen studies met the inclusion criteria. Under short-term aging conditions, NMA showed higher bond strengths in the uncontaminated LS₂ group and the contaminated groups cleaned with hydrofluoric acid etching, commercial ceramic cleaning agents, or phosphoric acid etching than those uncleaned or cleaned with water or alcohol. In long-term aging, PMA and NMA exhibited higher bond strengths in the uncontaminated LS₂ group and contaminated group cleaned with phosphoric acid etching than those contaminated groups cleaned with water or alcohol.

Conclusions: Phosphoric acid etching effectively eliminates contaminants, whereas the application of water or alcohol is ineffective. Although ceramic cleaning agents effectively decontaminate surfaces under short-term aging conditions, their performance deteriorates over time, potentially compromising long-term bond durability.

Purpose: This study compared the scanned data obtained from four conventional silicone impressions using a laboratory scanner to investigate the effects of impression materials on accuracy and precision.

Methods: The master model was a gauge with four small height increments constructed from an assembly of ceramic gauge blocks. Impressions of the master model were made using four silicone impression materials (Imprinsis Regular: blue; Fusion II: pink; Fit Checker: white, Imprint3: yellow). The impressions were scanned under blue light using a laboratory scanner. The points of inquiry were 1) advisability of three-dimensional (3D) reconstruction, 2) reproducibility at the surface level, and 3) microstep detectability. Data were analyzed using a one-way analysis of variance and Tukey’s multiple comparison test.

Results: 3D reconstruction was achieved for all impressions except Imprint3. The scanning performance of the impression material affected the spectral reflection factor. There was no significant difference in surface reproducibility among the three impression materials, and the detectability of small step height in Imprinsis Regular was significantly better than that in other impression materials (P < 0.05).

Conclusions: The color of the impression material affected digitization when a blue-light scanner was used. The digitization accuracy of Imprinsis Regular was higher than that of the other impression materials. However, the digitization accuracy of the three impression materials that could be 3D-reconstructed was within clinical tolerance. These findings can broaden the clinical applications of digital dentistry and mitigate the errors associated with dental materials.

Purpose: To investigate the association of removable partial denture (RPD)/complete denture (CD) and fixed partial denture (FPD) use with health-related quality of life (HRQoL), evaluated using the EuroQol 5-dimension, 5-level instrument (EQ-5D-5L) among older adults with tooth loss.

Methods: Cross-sectional data from the 2022 Japan Gerontological Evaluation Study. Participants were classified as RPD/CD users, FPD users, or nonusers. Generalized linear regression models stratified by the number of teeth were used to explore the association of RPD/CD and FPD use with the HRQoL evaluated using the EQ-5D-5L. The interactions among the number of teeth, RPD/CD, and FPD use were examined.

Results: After adjusting for all covariates, RPD/CD and FPD users with 5–19 teeth had higher predicted HRQoL utility scores than nonusers (RPD/CD [n=14,297]: 0.837 vs. 0.850; FPD [n=7,476]: 0.858 vs. 0.861 [nonusers vs. users]). The interaction between number of teeth and RPD/CD and FPD use revealed that differences in HRQoL between users and nonusers were greater among those with 1–14 teeth for RPD/CD use and among those with 10–14 teeth for FPD use (RPD/CD: β = −0.008, P < 0.01; FPD: β = −0.008, P = 0.183).

Conclusions: RPD/CD and FPD users with 5–19 teeth had a higher HRQoL than nonusers, with differences appearing to depend on the number of teeth. The World Health Organization has promoted the incorporation of oral health into universal health coverage (UHC). Given the minimal HRQoL difference between CD users and nonusers among edentulous individuals, unconventional dentures may merit inclusion in UHC.