Electrocardiogram-based artificial intelligence for the diagnosis of heart failure:a systematic review and meta-analysis

作     者：Xin-Mu LI Xin-Yi GAO Gary Tse Shen-Da HONG Kang-Yin CHEN Guang-Ping LI Tong LIU Xin-Mu LI;Xin-Yi GAO;Gary Tse;Shen-Da HONG;Kang-Yin CHEN;Guang-Ping LI;Tong LIU

作者机构：Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular DiseaseDepartment of CardiologyTianjin Institute of CardiologySecond Hospital of Tianjin Medical UniversityTianjinChina Kent and Medway Medical SchoolCanterburyUnited Kingdom National Institute of Health Data Science at Peking UniversityPeking UniversityBeijingChina Institute of Medical TechnologyPeking University Health Science CenterBeijingChina 

出 版 物：《Journal of Geriatric Cardiology》 (老年心脏病学杂志（英文版）)

年 卷 期：2022年第19卷第12期

页      面：970-980,1011-1015页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 1002[医学-临床医学] 08[工学] 1010[医学-医学技术（可授医学、理学学位）] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 10[医学] 

基　　金：supported by the National Natural Science Foundation of China(No.81970270&No.82170327) the Tianjin Natural Science Foundation(20JC ZDJC00340&20JCZXJC00130) the Tianjin Key Medical Discipline(Specialty)Construction Project(TJYXZDXK-029A) 

主　　题：diagnosis assessed specificity 

摘      要：BACKGROUND The electrocardiogram(ECG)is an inexpensive and easily accessible investigation for the diagnosis of cardiovascular diseases including heart failure(HF).The application of artificial intelligence(AI)has contributed to clinical practice in terms of aiding diagnosis,prognosis,risk stratification and guiding clinical *** aim of this study is to systematically review and perform a meta-analysis of published studies on the application of AI for HF detection based on the *** We searched Embase,PubMed and Web of Science databases to identify literature using AI for HF detection based on ECG *** quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2(QUADAS-2)***-effects models were used for calculating the effect estimates and hierarchical receiver operating characteristic curves were *** analysis was *** and the risk of bias were also *** A total of 11 studies including 104,737 subjects were *** area under the curve for HF diagnosis was 0.986,with a corresponding pooled sensitivity of 0.95(95%CI:0.86–0.98),specificity of 0.98(95%CI:0.95–0.99)and diagnostic odds ratio of 831.51(95%CI:127.85–5407.74).In the patient selection domain of QUADAS-2,eight studies were designated as high *** According to the available evidence,the incorporation of AI can aid the diagnosis of ***,there is heterogeneity among machine learning algorithms and improvements are required in terms of quality and study design.