RESUMO
The superimposition of sequential radiographs of the head is commonly used to determine the amount and direction of orthodontic tooth movement. A harmless method includes the timely unlimited superimposition on the relatively stable palatal rugae, but the method is performed manually and, if automated, relies on the best fit of surfaces, not only rugal structures. In the first step, motion estimation requires segmenting and detecting the location of teeth and rugae at any time during the orthodontic intervention. Aim: to develop a process of tooth segmentation that eliminates all manual steps to achieve an autonomous system of assessment of the dentition. Methods: A dataset of 797 occlusal views from photographs of teeth was created. The photographs were manually semantically segmented and labeled. Machine learning methods were applied to identify a robust deep network architecture able to semantically segment teeth in unseen photographs. Using well-defined metrics such as accuracy, precision, and the average mean intersection over union (mIoU), four network architectures were tested: MobileUnet, AdapNet, DenseNet, and SegNet. The robustness of the trained network was additionally tested on a set of 47 image pairs of patients before and after orthodontic treatment. Results: SegNet was the most accurate network, producing 95.19% accuracy and an average mIoU value of 86.66% for the main sample and 86.2% for pre- and post-treatment images. Conclusions: Four architectural tests were developed for automated individual teeth segmentation and detection in two-dimensional photos that required no post-processing. Accuracy and robustness were best achieved with SegNet. Further research should focus on clinical applications and 3D system development.
RESUMO
PURPOSE: Driven by the shortage in qualified nurses and the high percentage of aging populations, the past decade has witnessed a significant growth in the use of robots in nursing, especially in countries like Japan. This article is a scoping review of the different tracks in which robots are used in nursing. Whereas assistive robots are used for physical care, including service and monitoring tasks, social assistive robots focus on the cognitive and emotional well-being of patients in need of companionship. METHODS: A total of six electronic databases were used in the search for journal papers and conference proceedings. The key words used in searching the databases were nursing OR nurses, AND robots OR robotics. Topics covering surgical robotics, nursing education robotics, and clinical procedures were excluded. FINDINGS: A total of 1,758 articles were retrieved, from which 69 articles were included in the final review. The analysis of the chosen papers led to the categorization of robots into two main categories: assistive robots and social assistive robots. CONCLUSIONS: After a detailed review of the state of the art in assistive robots and social assistive robots, an insight into the future of robotics in this field is provided. The recommendations include the need to intensify research on human robot interaction, greater focus on monitoring robots, and analysis of the psychological barriers that need to be surmounted to achieve more tolerance and higher acceptance of robots. CLINICAL RELEVANCE: For researchers and developers to provide suitable technological solutions, a full understanding of robotics in nursing is needed. An overview of the most recent applications and their proper categorization is key to finding areas for contribution.
