心电图跟踪图像技术应用于新型冠状病毒感染辅助诊断的新进展  

New advances in application of ECG tracking image technology for auxiliary diagnosis of novel coronavirus infection

作　　者：秦欢 刘璐[1] 查竣仁 陈荔红 秦静 张树龙[1] QIN Huan;LIU Lu;ZHA Junren;CHEN Lihong;QIN Jing;ZHANG Shulong(Heart Center,Affiliated Zhongshan Hospital of Dalian University,Dalian Liaoning 116001,China)

机构地区：[1]大连大学附属中山医院心脏中心,辽宁大连116001

出　　处：《实用心电学杂志》2023年第3期190-194,共5页Journal of Practical Electrocardiology

基　　金：辽宁省教育厅科学研究经费资助项目(LJKZ1191)。

摘　　要：新型冠状病毒(简称新冠病毒)感染疫情暴发以来,深度学习被广泛应用于疾病流行趋势预测、高危人群筛查及早期诊断等方面。新冠病毒感染主要导致肺部疾病,还会影响心血管系统,造成心电图异常。心电图跟踪图像技术是深度学习研究的热点,与传统的心电图判定方法相比,其精确度和敏感性都更高,可用于对新冠病毒感染患者进行有效诊断、临床决策和预后评估。本文将对新冠病毒感染后的心电图变化,以及心电图追踪图像技术在新冠病毒感染辅助诊断中的应用进展进行综述,以期为临床医护工作者提供参考。Since the outbreak of corona virus disease 2019,deep learning has been widely used in predicting epidemic trends,screening high-risk groups and making early diagnosis.Novel coronavirus infection mainly causes pulmonary diseases,and also affects cardiovascular system,resulting in electrocardiogram(ECG)abnormalities.ECG tracking image technology is a hot spot in the research of deep learning.Compared with traditional ECG judgment methods,its accuracy and sensitivity are both higher.It can be used in the effective diagnosis,clinical decision-making and prognosis evaluation of patients with novel coronavirus infection.This paper reviews on the ECG changes after novel coronavirus infection,and the advances of ECG tracking image technology applied in the auxiliary diagnosis of novel coronavirus infection,so as to provide references for clinical healthcare workers.

关 键 词：新型冠状病毒感染 心电图 心电图跟踪图像技术 人工智能 深度学习 辅助诊断 

分 类 号：R563.1[医药卫生—呼吸系统] R540.41[医药卫生—内科学]