Electrocardiogram-based artificial intelligence for the diagnosis of heart failure: a systematic review and meta-analysis
Xin-Mu LI1
Xin-Yi GAO1
Gary Tse2
Shen-Da HONG3
Kang-Yin CHEN1
Guang-Ping LI1
Tong LIU1
1.Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease,Department of Cardiology,Tianjin In-stitute of Cardiology,Second Hospital of Tianjin Medical University,Tianjin,China2.Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease,Department of Cardiology,Tianjin In-stitute of Cardiology,Second Hospital of Tianjin Medical University,Tianjin,China;Kent and Medway Medical School,Canterbury,United Kingdom3.National Institute of Health Data Science at Peking University,Peking University,Beijing,China;Institute of Medical Technology,Peking University Health Science Center,Beijing,China
摘要：BACKGROUND The electrocardiogram (ECG) is an inexpensive and easily accessible investigation for the diagnosis of cardi-ovascular 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 management. The aim of this study is to systematic-ally review and perform a meta-analysis of published studies on the application of AI for HF detection based on the ECG.METHODS We searched Embase, PubMed and Web of Science databases to identify literature using AI for HF detection based on ECG data. The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUA-DAS-2) criteria. Random-effects models were used for calculating the effect estimates and hierarchical receiver operating charac-teristic curves were plotted. Subgroup analysis was performed. Heterogeneity and the risk of bias were also assessed. RESULTS A total of 11 studies including 104,737 subjects were included. The 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 ra-tio of 831.51 (95％ CI: 127.85–5407.74). In the patient selection domain of QUADAS-2, eight studies were designated as high risk. CONCLUSIONS According to the available evidence, the incorporation of AI can aid the diagnosis of HF. However, there is heterogeneity among machine learning algorithms and improvements are required in terms of quality and study design.
机标关键词：meta-analysisintelligencetrocarreviewheartgramcardardi
论文发表日期：2022-12-28
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：11( 970-980 )