Artificial intelligence in ECG-based diagnosis of low left ventricular ejection fraction: a systematic review and meta-analysis

Kemp CD, Conte JV. The pathophysiology of heart failure. Cardiovasc Pathol. 2012;21(5):365–71. https://doi.org/10.1016/j.carpath.2011.11.007.

Article  Google Scholar 

Vasan RS, Xanthakis V, Lyass A, Andersson C, Tsao C, Cheng S, et al. Epidemiology of left ventricular systolic dysfunction and heart failure in the Framingham study. JACC Cardiovasc Imaging. 2018;11(1):1–11. https://doi.org/10.1016/j.jcmg.2017.08.007.

Article  Google Scholar 

Wang TJ, Evans JC, Benjamin EJ, Levy D, LeRoy EC, Vasan RS. Natural history of asymptomatic left ventricular systolic dysfunction in the community. Circulation. 2003;108(8):977–82. https://doi.org/10.1161/01.CIR.0000085166.44904.79.

Article  Google Scholar 

Sangha V, Nargesi AA, Dhingra LS, Khunte A, Mortazavi BJ, Ribeiro AH, et al. Detection of left ventricular systolic dysfunction from electrocardiographic images. Circulation. 2023;148(9):765–77. https://doi.org/10.1161/CIRCULATIONAHA.122.062646.

Article  Google Scholar 

Olesen LL, Andersen A. ECG as a first step in the detection of left ventricular systolic dysfunction in the elderly. ESC Heart Fail. 2016;3(1):44–52. https://doi.org/10.1002/ehf2.12067.

Article  Google Scholar 

Lin G-M, Lu HH-S. Electrocardiographic machine learning to predict left ventricular diastolic dysfunction in Asian young male adults. IEEE Access. 2021;9:49047–54. https://doi.org/10.1109/ACCESS.2021.3069232.

Article  Google Scholar 

Chaui-Berlinck JG, Monteiro LHA. Frank-Starling mechanism and short-term adjustment of cardiac flow. J Exp Biol. 2017;220(23):4391–8. https://doi.org/10.1242/jeb.167106.

Article  Google Scholar 

Choi J, Lee S, Chang M, Lee Y, Oh GC, Lee H-Y. Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction. Sci Rep. 2022;12:14235. https://doi.org/10.1038/s41598-022-18640-8.

Article  Google Scholar 

Liang X, Jiang N, Qi P, Chen Z, Tong J, Xia S. ECGEL: a multimodal 12-lead ECG classification model for heart failure prediction. Biomed Eng Lett. 2025;1–11. https://doi.org/10.1007/s13534-025-00468-6.

Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE, Colvin MM, et al. 2017 ACC/AHA/HFSA focused update of the 2013 ACCF/AHA guideline for the management of heart failure: A report of the American college of cardiology/american heart association task force on clinical practice guidelines and the heart failure society of America. Circulation. 2017;136(6):e137–61. https://doi.org/10.1161/CIR.0000000000000509.

Article  Google Scholar 

Bozkurt B, Coats AJ, Tsutsui H, Abdelhamid M, Adamopoulos S, Albert N, et al. Universal definition and classification of heart failure: A report of the heart failure society of America, heart failure association of the European society of cardiology, Japanese heart failure society and writing committee of the universal definition of heart failure. Eur J Heart Fail. 2021;23(3):352–80. https://doi.org/10.1002/ejhf.2115.

Article  Google Scholar 

Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and Meta-Analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097. https://doi.org/10.1371/journal.pmed.1000097.

Article  Google Scholar 

Tyagi PK, Agarwal D. Systematic review of automated sleep apnea detection based on physiological signal data using deep learning algorithm: a meta-analysis approach. Biomed Eng Lett. 2023;13(3):293–312. https://doi.org/10.1007/s13534-023-00297-5.

Article  Google Scholar 

Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al. editors. Cochrane handbook for systematic reviews of interventions. Hoboken: Wiley; 2019. p. 4. https://doi.org/10.1002/9781119536604.

Book  Google Scholar 

Whiting PF. QUADAS-2: A revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529–36. https://doi.org/10.7326/0003-4819-155-8-201110180-00009.

Article  Google Scholar 

Wang Y, Wang J, Zhang X, Xia C, Wang Z. Diagnostic efficacy of long non-coding RNAs in multiple sclerosis: a systematic review and meta-analysis. Front Genet. 2024;15:1400387. https://doi.org/10.3389/fgene.2024.1400387.

Article  Google Scholar 

Ma X, Zeng H, Zhang J, Chen L, Jia H. New practical methods to obtain crucial data in performing diagnostic meta-analysis of the published literature. J Evid Based Med. 2018;11(1):56–63. https://doi.org/10.1111/JEBM.12281.

Article  Google Scholar 

Shim SR, Kim S-J, Lee J. Diagnostic test accuracy: application and practice using R software. Epidemiol Health. 2019;41:e2019007. https://doi.org/10.4178/epih.e2019007.

Article  Google Scholar 

Bhardwaj A, Budaraju D, Venkatesh P, Chowdhury D, Kumar RP, Pal K, et al. A holistic overview of artificial intelligence in detection, classification and prediction of atrial fibrillation using electrocardiogram: A systematic review and meta-analysis. Arch Comput Methods Eng. 2023;30(7):4063–79. https://doi.org/10.1007/s11831-023-09935-8.

Article  Google Scholar 

Heinen A, Valdesogo A. Spearman rank correlation of the bivariate student t and scale mixtures of normal distributions. J Multivar Anal. 2020;179:104650. https://doi.org/10.1016/j.jmva.2020.104650.

Article  MathSciNet  Google Scholar 

Nyaga VN, Arbyn M. Metadta: a Stata command for meta-analysis and meta-regression of diagnostic test accuracy data– a tutorial. Archives Public Health. 2022;80:216. https://doi.org/10.1186/s13690-021-00747-5.

Article  Google Scholar 

Deeks JJ, Macaskill P, Irwig L. The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. J Clin Epidemiol. 2005;58(9):882–93. https://doi.org/10.1016/J.JCLINEPI.2005.01.016.

Article  Google Scholar 

Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629–34. https://doi.org/10.1136/BMJ.315.7109.629.

Article  Google Scholar 

Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication Bias. Biometrics. 1994;50(4):1088–101. https://doi.org/10.2307/2533446.

Article  Google Scholar 

Carbonati T, Eslami P, Waks JW, Fiorina L, Chaudhari A, Henry C et al. Deep neural networks detect regional wall motion abnormalities and preclinical cardiovascular disease from 12-lead ECGs. 2024. https://doi.org/10.1101/2024.05.31.24308304

Hughes JW, Somani S, Elias P, Tooley J, Rogers AJ, Poterucha T, et al. Simple models vs. deep learning in detecting low ejection fraction from the electrocardiogram. Eur Heart J - Digit Health. 2024;5(4):427–34. https://doi.org/10.1093/ehjdh/ztae034.

Article  Google Scholar 

Zhong G, Wang Y, Liu S, Deng X, Wang A, Yang C. Predicting ejection fraction from electrocardiogram signals using a multi-task learning model. 2023 IEEE 19th International Conference on Body Sensor Networks (BSN). IEEE; 2023. pp. 1–5. https://doi.org/10.1109/BSN58485.2023.10331174

Huang Y-C, Hsu Y-C, Liu Z-Y, Lin C-H, Tsai R, Chen J-S, et al. Artificial intelligence-enabled electrocardiographic screening for left ventricular systolic dysfunction and mortality risk prediction. Front Cardiovasc Med. 2023;10:1070641. https://doi.org/10.3389/fcvm.2023.1070641.

Article  Google Scholar 

Lee C-H, Liu W-T, Lou Y-S, Lin C-S, Fang W-H, Lee C-C, et al. Artificial intelligence-enabled electrocardiogram screens low left ventricular ejection fraction with a degree of confidence. Digit Health. 2022;8:205520762211432. https://doi.org/10.1177/20552076221143249.

Article  Google Scholar 

Golany T, Radinsky K, Kofman N, Litovchik I, Young R, Monayer A, et al. Physicians and machine-learning algorithm performance in predicting left-ventricular systolic dysfunction from a standard 12-lead-electrocardiogram. J Clin Med. 2022;11(22):6767. https://doi.org/10.3390/jcm11226767.

Article  Google Scholar 

Honarvar H, Agarwal C, Somani S, Vaid A, Lampert J, Wanyan T, et al. Enhancing convolutional neural network predictions of electrocardiograms with left ventricular dysfunction using a novel sub-waveform representation. Cardiovasc Digit Health J. 2022;3(5):220–31. https://doi.org/10.1016/j.cvdhj.2022.07.074.

Article  Google Scholar 

Harmon DM, Carter RE, Cohen-Shelly M, Svatikova A, Adedinsewo DA, Noseworthy PA, et al. Real-world performance, long-term efficacy, and absence of bias in the artificial intelligence enhanced electrocardiogram to detect left ventricular systolic dysfunction. Eur Heart J - Digit Health. 2022;3(2):238–44. https://doi.org/10.1093/ehjdh/ztac028.

Article  Google Scholar 

Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, et al. Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nat Med. 2019;25:70–4. https://doi.org/10.1038/s41591-018-0240-2.

Article  Google Scholar 

Chen H-Y, Lin C-S, Fang W-H, Lou Y-S, Cheng C-C, Lee C-C, et al. Artificial Intelligence-enabled electrocardiography predicts left ventricular dysfunction and future cardiovascular outcomes: A retrospective analysis. J Personalized Med. 2022;12(3):455. https://doi.org/10.3390/jpm12030455.

Article  Google Scholar 

Vaid A, Johnson KW, Badgeley MA, Somani SS, Bicak M, Landi I, et al. Using deep-learning algorithms to simultaneously identify right and left ventricular dysfunction from the electrocardiogram. JACC Cardiovasc Imaging. 2022;15(3):395–410. https://doi.org/10.1016/j.jcmg.2021.08.004.

Article  Google Scholar 

Katsushika S, Kodera S, Nakamoto M, Ninomiya K, Inoue S, Sawano S, et al. The effectiveness of a deep learning model to detect left ventricular systolic dysfunction from electrocardiograms. Int Heart J. 2021;62(6):1332–41. https://doi.org/10.1536/ihj.21-407.

Article  Google Scholar 

Kashou AH, Medina-Inojosa JR, Noseworthy PA, Rodeheffer RJ, Lopez-Jimenez F, Attia IZ et al. Artificial Intelligence–augmented electrocardiogram detection of left ventricular systolic dysfunction in the general population. Mayo Clinic Proceedings. 2021;96(10):2576–2586. https://doi.org/10.1016/j.mayocp.2021.02.029

Cho J, Lee B, Kwon J-M, Lee Y, Park H, Oh B-H, et al. Artificial intelligence algorithm for screening heart failure with reduced ejection fraction using electrocardiography. ASAIO J. 2021;67(3):314–21. https://doi.org/10.1097/MAT.0000000000001218.

Article  Google Scholar 

Jentzer JC, Kashou AH, Attia ZI, Lopez-Jimenez F, Kapa S, Friedman PA, et al. Left ventricular systolic dysfunction identification using artificial intelligence-augmented electrocardiogram in cardiac intensive care unit patients. Int J Cardiol. 2021;326:114–23. https://doi.org/10.1016/j.ijcard.2020.10.074.

Article  Google Scholar 

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