Background: Regenerative endodontic procedures (REPs) are promising treatments for immature teeth with necrotic pulp. Artificial intelligence (AI) is increasingly used in dentistry; thus, this study evaluates the reliability of AI-generated information on REPs, comparing four AI models against clinical guidelines. Methods: ChatGPT-4o, Claude 3.5 Sonnet, Grok 2, and Gemini 2.0 Advanced were tested with 20 REP-related questions from the ESE/AAE guidelines and expert consensus. Questions were posed in Turkish and English, with or without prompts. Two specialists assessed 640 AI-generated answers via a four-point rubric. Inter-rater reliability and response accuracy were statistically analyzed. Results: Inter-rater reliability was high (0.85–0.97). ChatGPT-4o showed higher accuracy with English prompts (p < 0.05). Claude was more accurate than Grok in the Turkish (nonprompted) and English (prompted) conditions (p < 0.05). No model reached ≥80% accuracy. Claude (English, prompted) scored highest; Grok-Turkish (nonprompted) scored lowest. Conclusions: The performance of AI models varies significantly across languages. English queries yield higher accuracy. While AI shows potential for REPs information, current models lack sufficient accuracy for clinical reliance. Cautious interpretation and validation against guidelines are essential. Further research is needed to enhance AI performance in specialized dental fields.

Image processing with artificial intelligence has shown significant promise in various medical imaging applications. The present study aims to evaluate the performance of 16 different convolutional neural networks (CNNs) in predicting age and gender from panoramic radiographs in children and young adults. The networks tested included DarkNet-19, DarkNet-53, Inception-ResNet-v2, VGG-19, DenseNet-201, ResNet-50, GoogLeNet, VGG-16, SqueezeNet, ResNet-101, ResNet-18, ShuffleNet, MobileNet-v2, NasNet-Mobile, AlexNet, and Xception. These networks were trained on a dataset of 7336 radiographs from individuals aged between 5 and 21. Age and gender estimation accuracy and mean absolute age prediction errors were evaluated on 340 radiographs. Statistical analyses were conducted using Shapiro–Wilk, one-way ANOVA, and Tukey tests (p < 0.05). The gender prediction accuracy and the mean absolute age prediction error were, respectively, 87.94% and 0.582 for DarkNet-53, 86.18% and 0.427 for DarkNet-19, 84.71% and 0.703 for GoogLeNet, 81.76% and 0.756 for DenseNet-201, 81.76% and 1.115 for ResNet-18, 80.88% and 0.650 for VGG-19, 79.41% and 0.988 for SqueezeNet, 79.12% and 0.682 for Inception-Resnet-v2, 78.24% and 0.747 for ResNet-50, 77.35% and 1.047 for VGG-16, 76.47% and 1.109 for Xception, 75.88% and 0.977 for ResNet-101, 73.24% and 0.894 for ShuffleNet, 72.35% and 1.206 for AlexNet, 71.18% and 1.094 for NasNet-Mobile, and 62.94% and 1.327 for MobileNet-v2. No statistical difference in age prediction performance was found between DarkNet-19 and DarkNet-53, which demonstrated the most successful age estimation results. Despite these promising results, all tested CNNs performed below 90% accuracy and were not deemed suitable for clinical use. Future studies should continue with more-advanced networks and larger datasets.

In resins produced with a 3D printer, the printing parameters affect the properties of the restoration produced. This study examined the effect of the printing angle and post-curing time on the optical properties of temporary restorations. A total of 135 disk-shaped Formlabs temporary resins (10 × 2 mm) were produced at three different printing angles (0, 45, and 90 degrees) and post-cured for three different times (20, 40, and 60 min) (n = 15). Color and translucency measurements were taken for each group with a spectrophotometer (VITA Easyshade V). The ΔE values between printing angles and curing times influence each other. The highest color change was observed in the groups produced with a 90° printing angle. Considering the post-curing times, the highest color change was observed in the groups cured for 40 min. Increasing the curing time from 20 to 40 min decreases the translucency, whereas further increasing the curing time does not significantly affect the translucency. In terms of the impact on the translucency caused by the printing angles, 0° exhibited a lower translucency compared to other printing angles. During the 3D printing of temporary prostheses, both printing angles and post-curing times can affect their optical properties.

Aim: The increased risk of caries during orthodontic treatment is a significant prevention challenge. This in vitro study aimed to evaluate the caries-preventive potential of bioactive composites used in clear aligner attachments. Methods: A bioactive composite group consisted of Activa Bioactive Restorative (Pulpdent, USA), while a traditional flowable composite group included G-Aenial Universal Flo (GC, Japan). A total of 24 sound extracted human teeth were cleaned using pumice, and all surfaces except for a 3 × 3 mm area on the buccal crown were coated with acid resistant varnish. Standard acid etching and bonding protocols were applied to the exposed area, after which clear aligner attachments were created using a mould. The specimens underwent pH cycling, consisting of immersion in a demineralising solution for 48 hours followed by immersion in a remineralising solution for 24 hours, repeated three times. Sections were obtained from the attachment areas, and demineralisation depths were measured using a digital microscope. Statistical analysis was performed using an independent samples t-test. Results: The mean lesion depth for the bioactive composite (Activa Bioactive Restorative) was 120 ± 29 μm, while the traditional flowable composite (G-Aenial Universal Flo) exhibited a mean lesion depth of 150 ± 41 μm. According to the t-test results, significantly less demineralisation depth was observed in the bioactive composite group (p < 0.05). Conclusion: Under in vitro conditions, bioactive composites exhibited lower demineralisation depths compared to traditional flowable composites. The findings suggest that bioactive composites may offer caries-preventive benefits when used in clear aligner attachments, although further studies are needed to confirm the effects.

Aim: This review examines the studies on resin composites within the past decade and evaluates the materials’ application methods and success through Fédération Dentaire Internationale (FDI) scores. Materials and Methods: Search results from three databases (a total of 3295 studies) were transferred to Mendeley Desktop (Mendeley, London, UK) software and deduplicated (2638 studies). Eight studies were included in this meta-analysis after scanning the title, abstract, and full-text screening. Risk of bias analysis was performed using the Cochrane Handbook tool. Shapiro–Wilk, Kolmogorov–Smirnov, Kruskal–Wallis, and post hoc Tamhane’s T2 were used for statistical analysis. Results: Conventional composite’s surface staining FDI score was higher than flowable and silorane composites (no difference between these two). Also, conventional composite has the highest score in terms of staining. Conventional composite + two-step self-etch adhesive and bulk fill composite + two-step self-etch adhesive have lower FDI scores in marginal staining. In noncarious cervical lesions (NCCL), conventional composite + two-step total-etch adhesive has a higher score than flowable composite + two-step self-etch adhesive. Flowable composite + two-step self-etch adhesive and conventional composite + single-step self-etch adhesive have the highest FDI scores for marginal adaptation (no statistical difference between them). Conventional composite + two-step self-etch adhesive and bulk fill composite + two-step self-etch have the lowest scores (no statistical difference between them). For recurrence of caries, all groups have Score 1, the best score, and there is no significant difference between them. Conclusions: Each restorative material and adhesive system has different advantages, and the practitioner must choose the most appropriate.