Institute of Chemical Technology and Engineering, Faculty of Chemical Technology, Poznan University of Technology, ul. Berdychowo 4, 60-965 Poznan, Poland.
Eurecat, Centre Tecnològic de Catalunya, C/Marcellí Domingo s/n, 43007 Tarragona, Spain.
Int J Environ Res Public Health. 2023 Mar 5;20(5):4605. doi: 10.3390/ijerph20054605.
The rapid advances in science and technology in the field of artificial neural networks have led to noticeable interest in the application of this technology in medicine. Given the need to develop medical sensors that monitor vital signs to meet both people's needs in real life and in clinical research, the use of computer-based techniques should be considered. This paper describes the latest progress in heart rate sensors empowered by machine learning methods. The paper is based on a review of the literature and patents from recent years, and is reported according to the PRISMA 2020 statement. The most important challenges and prospects in this field are presented. Key applications of machine learning are discussed in medical sensors used for medical diagnostics in the area of data collection, processing, and interpretation of results. Although current solutions are not yet able to operate independently, especially in the diagnostic context, it is likely that medical sensors will be further developed using advanced artificial intelligence methods.
人工神经网络领域的科学技术快速发展，使得人们对该技术在医学中的应用产生了浓厚的兴趣。鉴于需要开发监测生命体征的医疗传感器，以满足人们在现实生活和临床研究中的需求，应考虑使用基于计算机的技术。本文介绍了由机器学习方法驱动的心率传感器的最新进展。本文基于对近年来文献和专利的回顾，并根据 PRISMA 2020 声明进行报告。提出了该领域最重要的挑战和前景。讨论了机器学习在医疗传感器中的关键应用，这些传感器用于数据收集、处理和结果解释领域的医疗诊断。虽然目前的解决方案还不能独立运行，特别是在诊断方面，但使用先进的人工智能方法进一步开发医疗传感器是很有可能的。
