Chen Wei, Yi Zhe, Lim Lincoln Jian Rong, Lim Rebecca Qian Ru, Zhang Aijie, Qian Zhen, Huang Jiaxing, He Jia, Liu Bo
Department of Hand Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.
Department of Medical Imaging, Western Health, Footscray Hospital, Footscray, VIC, Australia.
Front Bioeng Biotechnol. 2024 Jul 17;12:1420100. doi: 10.3389/fbioe.2024.1420100. eCollection 2024.
In recent decades, there has been ongoing development in the application of computer vision (CV) in the medical field. As conventional contact-based physiological measurement techniques often restrict a patient's mobility in the clinical environment, the ability to achieve continuous, comfortable and convenient monitoring is thus a topic of interest to researchers. One type of CV application is remote imaging photoplethysmography (rPPG), which can predict vital signs using a video or image. While contactless physiological measurement techniques have an excellent application prospect, the lack of uniformity or standardization of contactless vital monitoring methods limits their application in remote healthcare/telehealth settings. Several methods have been developed to improve this limitation and solve the heterogeneity of video signals caused by movement, lighting, and equipment. The fundamental algorithms include traditional algorithms with optimization and developing deep learning (DL) algorithms. This article aims to provide an in-depth review of current Artificial Intelligence (AI) methods using CV and DL in contactless physiological measurement and a comprehensive summary of the latest development of contactless measurement techniques for skin perfusion, respiratory rate, blood oxygen saturation, heart rate, heart rate variability, and blood pressure.
近几十年来，计算机视觉（CV）在医学领域的应用一直在不断发展。由于传统的基于接触的生理测量技术常常会限制患者在临床环境中的活动能力，因此实现持续、舒适且便捷的监测能力成为了研究人员关注的课题。CV的一种应用类型是远程成像光电容积脉搏波描记法（rPPG），它可以通过视频或图像来预测生命体征。虽然非接触式生理测量技术具有出色的应用前景，但非接触式生命体征监测方法缺乏统一性或标准化，限制了它们在远程医疗保健/远程健康环境中的应用。人们已经开发了几种方法来改善这一局限性，并解决由运动、光照和设备导致的视频信号异质性问题。基本算法包括经过优化的传统算法和正在发展的深度学习（DL）算法。本文旨在深入综述当前在非接触式生理测量中使用CV和DL的人工智能（AI）方法，并全面总结皮肤灌注、呼吸频率、血氧饱和度、心率、心率变异性和血压等非接触式测量技术的最新进展。
