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Objective: This study sought to investigate the current status and faculty members' perceptions of oral care education in Bachelor of Nursing curriculums in Japan. Method: Data were gathered through a questionnaire survey of 196 participants, who were faculty members in charge of oral care education in the basic, adult, gerontological, and home nursing fields, from 295 nursing schools that offered Bachelor degrees in nursing across Japan. Results: The quantitative data showed that 38.5% of the participants worked for less than 5 years as oral care educators, only 15.5% taught oral care in perioperative wards, only 62.2% used an oral care training simulator, and less than 30% believed that training in the prevention of oral diseases should be enhanced. From the descriptive responses, this study identified such problems as the inability of students to perform student‐on‐student and patient‐based oral care training owing to the risk of SARS‐CoV‐2 transmission, and the lack of teaching materials, time, and human resources, and cooperation with other nursing fields and health professionals. Conclusions: This study revealed several problems in oral care education in the Bachelor of Nursing curriculums in Japan. To tackle these challenges, this study suggests the development of an interdisciplinary course that unifies oral care education in nursing fields, in order to promote collaborative oral care education and to improve nursing students' knowledge and skills of oral care. [ABSTRACT FROM AUTHOR]

Aims: The purpose of this study was to construct a model for oral assessment using deep learning image recognition technology and to verify its accuracy. Background: The effects of oral care on older people are significant, and the Oral Assessment Guide has been used internationally as an effective oral assessment tool in clinical practice. However, additional training, education, development of user manuals and continuous support from a dental hygienist are needed to improve the inter‐rater reliability of the Oral Assessment Guide. Design: A retrospective observational study. Methods: A total of 3,201 oral images of 114 older people aged >65 years were collected from five dental‐related facilities. These images were divided into six categories (lips, tongue, saliva, mucosa, gingiva, and teeth or dentures) that were evaluated by images, out of the total eight items that comprise components of the Oral Assessment Guide. Each item was classified into a rating of 1, 2 or 3. A convolutional neural network, which is a deep learning method used for image recognition, was used to construct the image recognition model. The study methods comply with the STROBE checklist. Results: We constructed models with a classification accuracy of 98.8% for lips, 94.3% for tongue, 92.8% for saliva, 78.6% for mucous membranes, 93.0% for gingiva and 93.6% for teeth or dentures. Conclusions: Highly accurate diagnostic imaging models using convolutional neural networks were constructed for six items of the Oral Assessment Guide and validated. In particular, for the five items of lips, tongue, saliva, gingiva, and teeth or dentures, models with a high accuracy of over 90% were obtained. Relevance to Clinical Practice: The model built in this study has the potential to contribute to obtain reproducibility and reliability of the ratings, to shorten the time for assessment, to collaborate with dental professionals and to be used as an educational tool. [ABSTRACT FROM AUTHOR]

Oral care is crucial to preventing diseases and maintaining the quality of life of elderly people. To create an effective training environment for nursing students, we developed a second prototype of an oral care simulator that can guide and record oral care practices. The simulator has three components: (1) a dentition model with pressure sensors, (2) a microcomputer to record signals, and (3) software for visualization. We proposed a novel mechanism to detect brushing behavior using pressure sensors and developed software to visualize the records of oral care practice. We calibrated the system to estimate the weights applied in the dentition model using a brush and verified that the calibration increased the accuracy of the estimation. [ABSTRACT FROM AUTHOR]