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.

This study aimed to investigate the performance of oral assessment and health care education at nursing schools across Japan to identify problems and the need for oral health professional support. The participants were the academic staff in charge of oral health care education in the basic, adult, gerontological, and home nursing fields of 295 schools in Japan that offered a bachelor's degree in nursing. A questionnaire was sent to 1180 heads of the nursing fields of these schools. They were requested to have oral health care educators complete the survey; data on the performance of oral assessment and health care education and human resources allocated were collected through a questionnaire. A total of 311 (26%) questionnaires were returned, 196 (63%) of which were completed by oral health care educators. Regarding the performance of oral assessment education, the majority (71%) of them spent less than 2 hours in teaching, and only 24.5% taught the usage of oral assessment tools. Regarding the performance of oral health care education, more than 90% spent less than 2 hours in lecture-based and practical oral health care education, respectively. Less than half taught the association of periodontal diseases with diabetes and cardiovascular diseases and use of fluoride for caries prevention in the lectures, and only approximately 30% taught the usage of an interspace brush or dental floss. Moreover, less than 10% of oral health professionals taught oral health care in lectures or practical oral health care. This study revealed problems associated with oral assessment and health care education in Japanese nursing schools. To address these, support from oral health care professionals is required. Further studies are also required to reveal problems in oral assessment and health care education in other nursing schools that do not offer a bachelor's degree programme in Japan and other countries.

Appropriate oral health care, depending on oral assessments, reduces the onset of pneumonia. However, the relationship between risk evaluation using an oral assessment tool and pneumonia in residents of long-term care facilities has not been fully elucidated. In the present study, we aim to examine the relationship between the total scores of the Oral Assessment Guide (OAG) and the incidence of pneumonia after a one-year baseline study of residents in long-term care facilities. The settings for sampling include nine long-term care facilities in Hokkaido. At baseline, there were 267 study subjects. A total of 72 individuals dropped out and 11 individuals met the exclusion criteria. Therefore, the subject sample included 184 individuals. Among the subjects included in our analyses, eight individuals developed pneumonia (six males and two females). A multiple logistic regression analysis was performed with the risk factors for developing pneumonia. Evaluations were performed based on the odds ratio (OR) and 95% confidence interval (CI). We observed that the OR for pneumonia onset was 2.29 (CI: 1.27–4.14) after being adjusted for pneumonia risk factors. Therefore, it was suggested that the total scores of the OAG could be used to screen for the risk of pneumonia onset in residents of long-term care and welfare facilities.

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.

Recently in aged society, the role of oral care is getting larger and larger because keeping oral health can bring aged people higher quality of life. So it is quite important to develop an educational environment in order to bring up nurses have practical oral care skills. In order to serve students in nursing an effective oral care training environment, we have been developing an oral care simulator. This paper describes the second prototype of the oral care simulator and its software. The system consists of 3 components; denture models with pressure sensors printed by 3D printer, a micro computer performs A/D conversion for signals, and a software visualizes recorded data. Many improvements from the 1st prototype are made including increased similarity to real human, and downsized devices. Calibration for the sensors was conducted, and we found more improvements for accurate detection of brush stroke weight are needed for practicality.