Purpose: This study aims to evaluate the accuracy of fit of full‐arch implant titanium frameworks fabricated from a fully digital workflow using a novel micro‐CT measurement technique. Materials and Methods: A 3D‐printed model with four implant analogs was fabricated. A baseline micro‐CT was obtained after placing temporary cylinders on the model. Next, the printed model was scanned with an intraoral scanner (TRIOS 5), and the STL files were used to fabricate 10 titanium frameworks. Each framework was placed back on the model, and another micro‐CT was taken under two conditions: single screw test (SST‐CT) and final fit test (FFT‐CT), and the measurements were compared to the baseline. Framework passivity was evaluated using a single‐screw test (SST) and a screw‐resistance test (SRT). The accuracy of the intraoral scans was assessed by superimposing the 10 scans with a laboratory scan STL to determine if the misfit was due to scanning or milling and designing errors. Results: None of the frameworks was deemed acceptable using SST‐CT, and only three had an acceptable fit using FFT‐CT. SST and SRT non‐passivity rates were 60% and 80%, respectively. Superimposition analysis revealed that only two intraoral scans used for framework fabrication fell within the acceptable deviation range of 150 microns, suggesting a high tendency for scanning errors and a possible milling or designing error in two samples. Conclusion: The results show a significant level of misfit. This suggests that the full‐digital workflow for full‐mouth rehabilitation can present some limitations. Due to the rapid advancement in intraoral scanning, further studies are required to validate these findings.

This article presents three cases outlining the benefits of utilizing a combination of digital and conventional workflows for the fabrication of immediate complete dentures. This combined digital and conventional approach addresses the recording of denture border extensions, digital articulation, and fabrication of complete dentures.

Purpose: The present study investigated the combined influence of four different scanning patterns and two intraoral scanning (IOS) technologies (Confocal Ultrafast Optical Sectioning vs. Active Triangulation with Structured Light) on the trueness and precision of both the intaglio and cameo surfaces of a maxillary reference denture. Materials and Methods: A maxillary complete arch reference denture was scanned using IOS technologies: Confocal Ultrafast Optical Sectioning (CUOS) and Active Triangulation with Structured Light (ATSL). Scanning was done 80 times with four strategies: APBOP (starting on the posterior intaglio surface), PABOP (beginning at the palatal aspect of the intaglio surface), BOPAP (starting on the cameo surface), and POBAP (beginning at the palatal cameo surface). Each strategy involved thorough scanning of various aspects, including occlusal surfaces, to capture the complete dentate arch. Distance–deviation values were calculated from comparisons with the STL reference model, and the data were analyzed using two‐way ANOVA and Tukey's test. Results: For the CUOS IOS technology, the POBAP scanning pattern exhibited the highest trueness (161 ± 6 μm) and precision (151 ± 7 μm), whereas the APBOP pattern showed the lowest trueness (183 ± 28 μm) and precision (171 ± 33 μm). For the ATSL IOS technology, the POBAP pattern also demonstrated the highest trueness (200 ± 32 μm) and precision (198 ± 32 μm), whereas the PABOP pattern showed the lowest trueness (241 ± 24 μm) and precision (239 ± 24 μm). The main effect of scanning technology was statistically significant ( p < 0.001), with the estimated marginal mean (EMM) for CUOS being 171 μm, indicating greater trueness relative to ATSL (211 μm). A partial eta‐squared value of 0.52 indicated that scanning technology explained approximately 52% of the total variation in trueness, representing a substantial effect size. Conclusion: CUOS consistently exhibited superior trueness across all scanning patterns compared with ATSL.

Aim: This study aimed to evaluate the effects of the material type and immersion solution on the color stability (ΔE) and surface roughness change (ΔRa) of provisional dental materials fabricated using subtractive milling or additive 3D printing techniques. Materials and Methods: Three provisional restorative materials were evaluated: one CAD/CAM-milled material (Vita CAD-Temp) and two 3D-printed materials (NextDent C&B MFH and Freeprint Crown [Detax]). The specimens were subjected to thermocycling and subsequently immersed for two weeks in coffee, hibiscus tea, Coca-Cola, or distilled water. Color change was evaluated using independent specimens (n = 10 per material × solution combination; N = 120), and ΔE was calculated from paired baseline and post-immersion CIELAB measurements. Surface roughness change was evaluated using a separate specimen set (n = 5 per material × solution combination; N = 60) and calculated as ΔRa = Ra_post-immersion−Ra_baseline. Factorial analyses were performed to assess the effects of the material, immersion solution, and their interaction, including multiplicity-adjusted post hoc comparisons where appropriate. Results: Color change was significantly affected by material type (p < 0.001), immersion solution (p < 0.001), and the material × solution interaction (p < 0.001). The interaction effect demonstrated that material performance depended strongly on the immersion solution. The highest mean ΔE was observed for NextDent exposed to hibiscus tea (8.96 ± 0.75), followed by Detax exposed to coffee (7.38 ± 1.69), whereas the lowest values were observed for milled material in distilled water (1.41 ± 0.45) and NextDent in cola (1.52 ± 1.00). For surface roughness, the immersion solution had a significant effect on ΔRa (p < 0.001), whereas the overall material effect (p = 0.136) and material × solution interaction (p = 0.706) were not statistically significant. Cola produced the greatest overall increase in surface roughness. Conclusions: The color stability of the tested provisional materials was dependent on the material type and immersion solution, with a pronounced interaction observed between these factors. The milled material generally demonstrated relatively low color changes across the tested conditions, while the responses of the 3D-printed materials varied substantially according to the immersion solution. In contrast, the change in surface roughness was influenced primarily by the immersion solution rather than material type. These findings indicate that the behaviors of provisional restorative materials should be interpreted according to the specific material–environment combination rather than the fabrication method alone.