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Virtual reality is considered to be useful in improving procedural skills in dental education, but systems using wearable devices such as head-mounted displays (HMDs) have many problems in terms of long-term use and hygiene, and the accuracy of stereoscopic viewing at close ranges is inadequate. We developed an abutment tooth formation simulator that utilizes a display (spatial reality display—SRD) to precisely reproduce 3D space with naked-eye stereoscopic viewing at close range. The purpose of this was to develop and validate the usefulness of an abutment tooth formation simulator using an SRD. A 3D-CG (three-dimensional computer graphics) dental model that can be cut in real time was output to the SRD, and an automatic quantitative scoring function was also implemented by comparing the cutting results with exemplars. Dentists in the department of fixed prosthodontics performed cutting operations on both a 2D display-based simulator and an SRD-based simulator and conducted a 5-point rating feedback survey. Compared to the simulator that used a 2D display, the measurements of the simulator using an SRD were significantly more accurate. The SRD-based abutment tooth formation simulator received a positive technical evaluation and high dentist satisfaction (4.37), suggesting its usefulness and raising expectations regarding its future application in dental education. [ABSTRACT FROM AUTHOR]

Extended Reality (XR) applications are considered useful for skill acquisition in dental education. In this study, we examined the functionality and usefulness of an application called "SR View for Endo" that measures root canal length using a Spatial Reality Display (SRD) capable of naked-eye stereoscopic viewing. Three-dimensional computer graphics (3DCG) data of dental models were obtained and output to both the SRD and conventional 2D display devices. Forty dentists working at the Kanagawa Dental University Hospital measured root canal length using both types of devices and provided feedback through a questionnaire. Statistical analysis using one-way analysis of variance evaluated the measurement values and time, while multivariate analysis assessed the relationship between questionnaire responses and measurement time. There was no significant difference in the measurement values between the 2D device and SRD, but there was a significant difference in measurement time. Furthermore, a negative correlation was observed between the frequency of device usage and the extended measurement time of the 2D device. Measurements using the SRD demonstrated higher accuracy and shorter measurement times compared to the 2D device, increasing expectations for clinical practice in dental education and clinical education for clinical applications. However, a certain percentage of participants experienced symptoms resembling motion sickness associated with virtual reality (VR). [ABSTRACT FROM AUTHOR]

There is presently a shortage of nurses in Japan, with a further shortage of 3,000–130,000 nurses expected. There is also shortage of scrub nurses. Scrub nurses are nurses who work in the operating room. The main job of scrub nurses is to assist surgeons. Scrub nurses are a high turnover rate, because it is a difficult job. Therefore, system for assisting scrub nurses are needed. The purpose of this study was to develop a robotic scrub nurse. As a first step, a detection system for surgical instruments was developed using the "Faster Region-Based Convolutional Neural Network" (Faster R-CNN). In experiments, computer graphics (CG) model images and 3D-printed model images were evaluated, and the system showed high accuracy. Consequently, the Faster R-CNN system can be considered as suitable for detecting surgical instruments. [ABSTRACT FROM AUTHOR]

The classification of the bacterium, Aeromonas caviae W-61 to Paenibacillus sp. strain W-61 is cloned and expressed on the basis of the nucleotide sequence of the 16S rRNA gene emended and the xyn5 gene encoding xylanese 5 (Xyn5) in Escherichia coli. Findings concluded that Paenibacillus sp. strain W-61 Xyn5 is a cell surface anchored modular xylanase possessing a functional cellulose binding module and SLH domains.

Purpose Large language models (LLMs) are increasingly employed across various fields, including medicine and dentistry. In the field of dental anesthesiology, LLM is expected to enhance the efficiency of information gathering, patient outcomes, and education. This study evaluates the performance of different LLMs in answering questions from the Japanese Dental Society of Anesthesiology Board Certification Examination (JDSABCE) to determine their utility in dental anesthesiology. Methods The study assessed three LLMs, ChatGPT-4 (OpenAI, San Francisco, California, United States), Gemini 1.0 (Google, Mountain View, California, United States), and Claude 3 Opus (Anthropic, San Francisco, California, United States), using multiple-choice questions from the 2020 to 2022 JDSABCE exams. Each LLM answered these questions three times. The study excluded questions involving figures or deemed inappropriate. The primary outcome was the accuracy rate of each LLM, with secondary analysis focusing on six subgroups: (1) basic physiology necessary for general anesthesia, (2) local anesthesia, (3) sedation and general anesthesia, (4) diseases and patient management methods that pose challenges in systemic management, (5) pain management, and (6) shock and cardiopulmonary resuscitation. Statistical analysis was performed using one-way ANOVA with Dunnett's multiple comparisons, with a significance threshold of p<0.05. Results ChatGPT-4 achieved a correct answer rate of 51.2% (95% CI: 42.78-60.56, p=0.003) and Claude 3 Opus 47.4% (95% CI: 43.45-51.44, p<0.001), both significantly higher than Gemini 1.0, which had a rate of 30.3% (95% CI: 26.53-34.14). In subgroup analyses, ChatGPT-4 and Claude 3 Opus demonstrated superior performance in basic physiology, sedation and general anesthesia, and systemic management challenges compared to Gemini 1.0. Notably, ChatGPT-4 excelled in questions related to systemic management (62.5%) and Claude 3 Opus in pain management (61.53%). Conclusions ChatGPT-4 and Claude 3 Opus exhibit potential for use in dental anesthesiology, outperforming Gemini 1.0. However, their current accuracy rates are insufficient for reliable clinical use. These findings have significant implications for dental anesthesiology practice and education, including educational support, clinical decision support, and continuing education. To enhance LLM utility in dental anesthesiology, it is crucial to increase the availability of high-quality information online and refine prompt engineering to better guide LLM responses., Competing Interests: Human subjects: All authors have confirmed that this study did not involve human participants or tissue. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Fujimoto et al.)