RESUMO
China is facing the peak of an ageing population, and there is an increase in demand for intelligent healthcare services for the elderly. The metaverse, as a new internet social communication space, has shown infinite potential for application. This paper focuses on the application of the metaverse in medicine in the intervention of cognitive decline in the elderly population. The problems in assessment and intervention of cognitive decline in the elderly group were analyzed. The basic data required to construct the metaverse in medicine was introduced. Moreover, it is demonstrated that the elderly users can conduct self-monitoring, experience immersive self-healing and health-care through the metaverse in medicine technology. Furthermore, we proposed that it is feasible that the metaverse in medicine has obvious advantages in prediction and diagnosis, prevention and rehabilitation, as well as assisting patients with cognitive decline. Risks for its application were pointed out as well. The metaverse in medicine technology solves the problem of non-face-to-face social communication for elderly users, which may help to reconstruct the social medical system and service mode for the elderly population.
Assuntos
Idoso , Humanos , Disfunção Cognitiva/prevenção & controle , Envelhecimento , China , Internet , TecnologiaRESUMO
Artificial neural network (ANN) is a network framework that drives artificial intelligence (AI). Classical convolutional neural networks (CNN) are mainly used for cell count and image recognition at fixed time in embryo evaluation. Fully connected deep neural networks (DNN), with increased accuracy of image recognition, are suitable for the units equipped with high configuration hardware and need comprehensive prediction according to the integrated clinical information. Residual networks improve the accuracy by increasing layers and solving the gradient disappearance problem through jump connection to realize dynamic embryo assessment. Bayesian networks (BN) and multi-layer perceptron (MLP) are two machine learning methods. The former is especially used for comprehensive prediction combined with complex clinical information in case of lack of conditions. The latter has gradient disappearance and explosion problem, and is easy to lose some spatial features of images, so it is used for small sample volumes. ANN has advantages in the prediction of implantation rate and aneuploidy and reducing invasive detection in quality assessment of embryos, which is an important research direction of human-assisted reproductive technology (ART).