Objective: This study investigated how the number of images collected for digital measurements in dentistry affects accuracy compared with traditional methods. Methods: A Frasaco maxillary model was scanned using a SHINING 3D AutoScan-DS-MIX dental 3D scanner to create an STL file. The maxilla was molded 10 times using polyvinyl siloxane (Zhermack Elite HD+) to produce plaster models, which were scanned with the same reference scanner to generate 10 STL files. The Frasaco model was scanned 10 times, capturing images in intervals of 800–1000, 1000–1200, and 1200–1500 using a 3Shape TRIOS 3 intraoral scanner, creating additional STL files. These were analyzed with reverse engineering software. Results: The most accurate measurements were obtained using 1200–1500 images. Conventional impression techniques performed significantly worse. There was a significant difference between the groups Digital 1200–1500 and Plaster (p < 0.001) and between Digital 800–1000 and Plaster (p = 0.007). No significant difference was found when the digital groups were compared among themselves. There was also no significant difference between the Plaster and Digital 1000–1200 groups. To compare precision values that were normally distributed across three or more methods, a one-way ANOVA was used. Trueness values that were not normally distributed with three or more methods were compared employing the Kruskal–Wallis test. Conclusions: Different image counts affect digital measurement accuracy. The most accurate measurements were obtained when collecting 1200–1500 images. Conventional impression techniques were shown to perform significantly worse than digital impression.

Occlusion plays a crucial role in maintaining masticatory function and temporomandibular joint (TMJ). Single implant supported restorations are widely used for posterior tooth replacement, but they require careful occlusal adjustment due to the absence of periodontal ligament. Digital occlusal analysis methods, such as digital impressions and Occlusense, provide quantitative assessments of occlusal contacts and force distribution. However, their accuracy and clinical relevance remain uncertain. In this prospective clinical study, occlusal force distribution was evaluated before and after placement of single implant supported restoration using the Medit i700 intraoral scanner and OccluSense system. Measurements were performed before and after prosthesis under standardised conditions. Occlusal contact areas and force distributions were analysed using CloudCompare and ImageJ software. Statistical analysis was performed using Kruskal–Wallis test and Kendall's Tau-B correlation analysis. A total of 20 patients were included in the study. Post-restoration measurements revealed significant changes in occlusal force distribution in different segments of the dental arch (p < 0.001). Strong correlations were observed between Medit and OccluSense measurements (p < 0.001). Single-unit implant restorations significantly alter the occlusal force distribution, affecting not only the restored tooth but also the adjacent and opposing teeth. Both Medit i700 and OccluSense provided valuable information, with OccluSense providing a more detailed representation of occlusal force density. These findings suggest that digital occlusal analysis methods can help optimise occlusal adjustments for implant restorations. The current study was registered in ClinicalTrials.gov (ID: NCT06862973) First posted: 07/03/2025. Retrospectively registered.

Preclinical training is essential in dental education for developing psychomotor and clinical skills. Conventional phantom models are widely used, though they lack real-time tactile feedback. This study aimed to compare the effectiveness of conventional phantom training with that of advanced simulation-based systems in improving dental students’ tooth preparation skills. This randomized controlled trial included 40 s-year dental students (N = 40) with no prior experience in simulation. Participants were randomly assigned to either conventional phantom training or virtual reality (VR) based simulation. Both groups received 10 h of training. Pre- and post-tests involved preparing tooth 46 with a chamfer finish line. Performances were assessed using five criteria: parallelism, occlusal reduction, taper angle, finish line standardization, and presence of undercuts. Each was scored on a 0–10 scale by a blinded evaluator. Statistical analyses were performed using ANOVA with post hoc tests at a significance level of p < .05. Both groups showed improvement in post-test scores The simulation group’s post-test performance was found to be significantly higher than that of the other groups. Post hoc analysis confirmed that the simulation group outperformed both its own pre-test and the conventional group’s post-test results. Simulation-based training led to significantly higher outcomes in tooth preparation compared to conventional methods. The integration of digital simulation into dental education may enhance clinical readiness by providing individualized, repeatable, and measurable learning environments.

Aim: This study aimed to assess how different restoration materials affect the trueness of intraoral scanners. Materials and Methods: Artificial teeth on a typodont model were prepared for crowns and fixed partial dentures (FPDs) using full metal, monolithic zirconia, and porcelain-fused-to-metal (PFM) materials. Each group underwent 10 scans with a Trios intraoral scanner, generating 60 STL files. A reference scanner created a master model using scanning spray to reduce reflection errors. The STL files were aligned with reverse engineering software for comparison and were analyzed in micrometers (μm) using Root Mean Square (RMS) and Mean Distance measurements. The Levene test and two-way ANOVA with Post Hoc analysis were used for statistical evaluation. Results: The RMS deviations for the FPDs were 77.9 ± 15.2 μm (full metal), 84.6 ± 6.9 μm (monolithic zirconia), and 130 ± 19.7 μm (PFM). For the crowns, the RMS values were 76.9 ± 6.5 μm (metal), 71 ± 8.2 μm (monolithic zirconia), and 153 ± 22.4 μm (PFM). The mean distance deviations for the FPDs were 11.4 ± 4.8 μm (metal), 11.2 ± 3.4 μm (monolithic zirconia), and 18.3 ± 2.6 μm (PFM). For the crowns, the mean distances were 8.6 ± 3.4 μm (metal), 10.2 ± 3 μm (monolithic zirconia), and 24.7 ± 3.3 μm (PFM). Significant differences were noted in the PFM groups. Conclusion: Restoration materials notably affected intraoral scanner trueness, especially PFM restorations. The restoration length did not significantly affect the accuracy.

Gözen M, et al. J Adv Prosthodont. 2026 Aug;18(4):362-373. https://doi.org/10.4047/jap.2026.18.4.362