From:  The diagnostic accuracy of artificial intelligence enhanced electrocardiography for the detection of cardiac dysfunction

 Summary of included studies.

AuthorYearTPFNFPTNAI/ML model typeECG typePrimary endpoint
Kwon et al. [18]20211,3203881,8448,403Deep learning12-lead, 6-lead, single-leadHFpEF
Sengupta et al. [19]201810726946Classical ML12-lead signal-processedAbnormal myocardial relaxation
Kuznetsova et al. [20]20223015214Classical MLSingle-lead (smartphone)LVDD
Kagiyama et al. [21]2020512562250Classical ML12-lead signal-processedLVDD/Abnormal relaxation
Lee et al. [22]202418,2053,67813,13663,744Deep learning12-leadIncreased filling pressure
Sabovčik et al. [23]2021171811441,011Classical ML12-leadLVDD
Unterhuber et al. [24]20219314465Deep learning12-leadHFpEF
Schlesinger et al. [25]20258363938482,543Deep learningSingle-lead (wearable)Elevated mPCWP
Gao et al. [26]202543171641Deep learning12-leadHFpEF risk (elevated LVEDP)

ECG: electrocardiogram; HFpEF: heart failure with preserved ejection fraction; LVDD: left ventricular diastolic dysfunction; ML: machine learning; AI: artificial intelligence.