Grün Dimitri, Rudolph Felix, Gumpfer Nils, Hannig Jennifer, Elsner Laura K, von Jeinsen Beatrice, Hamm Christian W, Rieth Andreas, Guckert Michael, Keller Till
Department of Internal Medicine I, Cardiology, Justus-Liebig University Giessen, Giessen, Germany.
Cognitive Information Systems, KITE - Kompetenzzentrum für Informationstechnologie, Technische Hochschule Mittelhessen - University of Applied Sciences, Friedberg, Germany.
Front Digit Health. 2021 Feb 25;2:584555. doi: 10.3389/fdgth.2020.584555. eCollection 2020.
Electrocardiography (ECG) is a quick and easily accessible method for diagnosis and screening of cardiovascular diseases including heart failure (HF). Artificial intelligence (AI) can be used for semi-automated ECG analysis. The aim of this evaluation was to provide an overview of AI use in HF detection from ECG signals and to perform a meta-analysis of available studies. An independent comprehensive search of the PubMed and Google Scholar database was conducted for articles dealing with the ability of AI to predict HF based on ECG signals. Only original articles published in peer-reviewed journals were considered. A total of five reports including 57,027 patients and 579,134 ECG datasets were identified including two sets of patient-level data and three with ECG-based datasets. The AI-processed ECG data yielded areas under the receiver operator characteristics curves between 0.92 and 0.99 to identify HF with higher values in ECG-based datasets. Applying a random-effects model, an sROC of 0.987 was calculated. Using the contingency tables led to diagnostic odds ratios ranging from 3.44 [95% confidence interval (CI) = 3.12-3.76] to 13.61 (95% CI = 13.14-14.08) also with lower values in patient-level datasets. The meta-analysis diagnostic odds ratio was 7.59 (95% CI = 5.85-9.34). The present meta-analysis confirms the ability of AI to predict HF from standard 12-lead ECG signals underlining the potential of such an approach. The observed overestimation of the diagnostic ability in artificial ECG databases compared to patient-level data stipulate the need for robust prospective studies.
心电图（ECG）是一种快速且易于获取的方法，用于诊断和筛查包括心力衰竭（HF）在内的心血管疾病。人工智能（AI）可用于半自动心电图分析。本评估的目的是概述AI在从心电图信号检测HF中的应用，并对现有研究进行荟萃分析。对PubMed和谷歌学术数据库进行了独立全面的搜索，以查找有关AI基于心电图信号预测HF能力的文章。仅考虑发表在同行评审期刊上的原创文章。共识别出5篇报告，包括57027例患者和579134个心电图数据集，其中包括两组患者水平的数据和三组基于心电图的数据集。经AI处理的心电图数据在接受者操作特征曲线下的面积在0.92至0.99之间，以识别HF，基于心电图的数据集的值更高。应用随机效应模型，计算出的sROC为0.987。使用列联表得出诊断比值比范围为3.44[95%置信区间（CI）=3.12 - 3.76]至13.61（95%CI = 13.14 - 14.08），患者水平数据集中的值也较低。荟萃分析诊断比值比为7.59（95%CI = 5.85 - 9.34）。本荟萃分析证实了AI从标准12导联心电图信号预测HF的能力，突出了这种方法的潜力。与患者水平数据相比，在人工心电图数据库中观察到的诊断能力高估表明需要进行强有力的前瞻性研究。
