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Introduction: Interest is growing in the potential of artificial intelligence (AI) chatbots and large language models like OpenAI's ChatGPT and Google's Gemini, particularly in dental education. To explore dental educators' perceptions of AI chatbots and large language models, specifically their potential benefits and challenges for dental education. Materials and Methods: A global cross‐sectional survey was conducted in May–June 2023 using a 31‐item online‐questionnaire to assess dental educators' perceptions of AI chatbots like ChatGPT and their influence on dental education. Dental educators, representing diverse backgrounds, were asked about their use of AI, its perceived impact, barriers to using chatbots, and the future role of AI in this field. Results: 428 dental educators (survey views = 1516; response rate = 28%) with a median [25/75th percentiles] age of 45 [37, 56] and 16 [8, 25] years of experience participated, with the majority from the Americas (54%), followed by Europe (26%) and Asia (10%). Thirty‐one percent of respondents already use AI tools, with 64% recognising their potential in dental education. Perception of AI's potential impact on dental education varied by region, with Africa (4[4–5]), Asia (4[4–5]), and the Americas (4[3–5]) perceiving more potential than Europe (3[3–4]). Educators stated that AI chatbots could enhance knowledge acquisition (74.3%), research (68.5%), and clinical decision‐making (63.6%) but expressed concern about AI's potential to reduce human interaction (53.9%). Dental educators' chief concerns centred around the absence of clear guidelines and training for using AI chatbots. Conclusion: A positive yet cautious view towards AI chatbot integration in dental curricula is prevalent, underscoring the need for clear implementation guidelines. [ABSTRACT FROM AUTHOR]

Purpose: Dual velocity encoding PC‐MRI can produce spurious artifacts when using high ratios of velocity encoding values (VENCs), limiting its ability to generate high‐quality images across a wide range of encoding velocities. This study aims to propose and compare dual‐VENC correction methods for such artifacts. Theory and Methods: Two denoising approaches based on spatiotemporal regularization are proposed and compared with a state‐of‐the‐art method based on sign correction. Accuracy is assessed using simulated data from an aorta and brain aneurysm, as well as 8 two‐dimensional (2D) PC‐MRI ascending aorta datasets. Two temporal resolutions (30,60) ms and noise levels (9,12) dB are considered, with noise added to the complex magnetization. The error is evaluated with respect to the noise‐free measurement in the synthetic case and to the unwrapped image without additional noise in the volunteer datasets. Results: In all studied cases, the proposed methods are more accurate than the Sign Correction technique. Using simulated 2D+T data from the aorta (60 ms, 9 dB), the Dual‐VENC (DV) error 0.82±0.07$$ 0.82\pm 0.07 $$ is reduced to: 0.66±0.04$$ 0.66\pm 0.04 $$ (Sign Correction); 0.34±0.04$$ 0.34\pm 0.04 $$ and 0.32±0.04$$ 0.32\pm 0.04 $$ (proposed techniques). The methods are found to be significantly different (p‐value <0.05$$ <0.05 $$). Importantly, brain aneurysm data revealed that the Sign Correction method is not suitable, as it increases error when the flow is not unidirectional. All three methods improve the accuracy of in vivo data. Conclusion: The newly proposed methods outperform the Sign Correction method in improving dual‐VENC PC‐MRI images. Among them, the approach based on temporal differences has shown the highest accuracy. [ABSTRACT FROM AUTHOR]

Background/Aim: This study aimed to develop a protocol that combines cone‐beam computed tomography (CBCT), software, and 3D printing to design 3D replicas for tooth autotransplantation. The goal was to evaluate the impact of this approach on the extraoral time of the donor teeth and the total surgical time, thereby enhancing surgical efficiency and outcomes. Materials and Methods: A non‐randomized trial (protocol 10.1186/ISRCTN13563091) was conducted at Riga Stradins University, enrolling 46 patients (13–22 years old) who required molar extraction and possessed a non‐erupted third molar. The patients were sequentially assigned to a 3D replica group (24 patients) or a control group (22 patients). The primary outcome measured was the extra‐alveolar time of the donor tooth, and the secondary outcome was the total duration of surgery. Both were assessed using a sample size capable of detecting a 10‐min difference. A generalized linear model adjusted for various factors was used to test for significant time differences (p <.05) between the groups. Results: Forty‐six patients were included in this analysis. The effect of using 3D replicas was not statistically significant and was associated with a decrease in the extraoral time of the donor tooth in seconds (β = −9.35, 95% CI [−40.86, 22.16]). For the total surgical time in minutes, the use of 3D replicas had a statistically significant impact, reducing the operation duration in minutes (β = −13.42, 95% CI [−24.50, −2.34]). No early complications were observed in either group, with all teeth present at 3–4 weeks post‐surgery. Conclusions: The integration of 3D printing technology can enhance the efficiency of autotransplantation surgeries, primarily by reducing surgical time. [ABSTRACT FROM AUTHOR]