Wang Xingyao, Li Qian, Ma Caiyun, Zhang Shuo, Lin Yujie, Li Jianqing, Liu Chengyu
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China.
State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2023 Dec 25;40(6):1084-1092. doi: 10.7507/1001-5515.202301032.
Electrocardiogram (ECG) monitoring owns important clinical value in diagnosis, prevention and rehabilitation of cardiovascular disease (CVD). With the rapid development of Internet of Things (IoT), big data, cloud computing, artificial intelligence (AI) and other advanced technologies, wearable ECG is playing an increasingly important role. With the aging process of the population, it is more and more urgent to upgrade the diagnostic mode of CVD. Using AI technology to assist the clinical analysis of long-term ECGs, and thus to improve the ability of early detection and prediction of CVD has become an important direction. Intelligent wearable ECG monitoring needs the collaboration between edge and cloud computing. Meanwhile, the clarity of medical scene is conducive for the precise implementation of wearable ECG monitoring. This paper first summarized the progress of AI-related ECG studies and the current technical orientation. Then three cases were depicted to illustrate how the AI in wearable ECG cooperate with the clinic. Finally, we demonstrated the two core issues-the reliability and worth of AI-related ECG technology and prospected the future opportunities and challenges.
心电图(ECG)监测在心血管疾病(CVD)的诊断、预防和康复中具有重要的临床价值。随着物联网(IoT)、大数据、云计算、人工智能(AI)等先进技术的快速发展,可穿戴式心电图正发挥着越来越重要的作用。随着人口老龄化进程的加快,升级心血管疾病的诊断模式变得越来越迫切。利用人工智能技术辅助长期心电图的临床分析,从而提高心血管疾病的早期检测和预测能力已成为一个重要方向。智能可穿戴式心电图监测需要边缘计算和云计算之间的协作。同时,医疗场景的清晰度有利于可穿戴式心电图监测的精确实施。本文首先总结了人工智能相关心电图研究的进展和当前的技术方向。然后描述了三个案例,以说明可穿戴式心电图中的人工智能如何与临床协作。最后,我们阐述了两个核心问题——人工智能相关心电图技术的可靠性和价值,并展望了未来的机遇和挑战。