Bottle, 2018, Routes to diagnosis of heart failure: observational study using linked data in England, Heart, 104, 600, 10.1136/heartjnl-2017-312183
Heidenreich, 2013, Forecasting the impact of heart failure in the United States: a policy statement from the American Heart Association, Circ Heart Fail, 6, 606, 10.1161/HHF.0b013e318291329a
Ambrosy, 2014, The global health and economic burden of hospitalizations for heart failure: lessons learned from hospitalized heart failure registries, J Am Coll Cardiol, 63, 1123, 10.1016/j.jacc.2013.11.053
Bozkurt, 2021, 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, Eur J Heart Fail, 23, 352, 10.1002/ejhf.2115
Solomon, 2019, Angiotensin-neprilysin inhibition in heart failure with preserved ejection fraction, N Engl J Med, 381, 1609, 10.1056/NEJMoa1908655
Burnett, 2017, Thirty years of evidence on the efficacy of drug treatments for chronic heart failure with reduced ejection fraction: a network meta-analysis, Circ Heart Fail, 10, 10.1161/CIRCHEARTFAILURE.116.003529
Bloom, 2017, Heart failure with reduced ejection fraction, Nat Rev Dis Primers, 3, 10.1038/nrdp.2017.58
Attia, 2019, Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram, Nat Med, 25, 70, 10.1038/s41591-018-0240-2
Adedinsewo, 2020, Artificial intelligence-enabled ECG algorithm to identify patients with left ventricular systolic dysfunction presenting to the emergency department with dyspnea, Circ Arrhythm Electrophysiol, 13, 10.1161/CIRCEP.120.008437
Attia, 2020, Artificial intelligence ECG to detect left ventricular dysfunction in COVID-19: a case series, Mayo Clin Proc, 95, 2464, 10.1016/j.mayocp.2020.09.020
Attia, 2019, Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction, J Cardiovasc Electrophysiol, 30, 668, 10.1111/jce.13889
Attia, 2021, External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction, Int J Cardiol, 329, 130, 10.1016/j.ijcard.2020.12.065
Yao, 2021, Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial, Nat Med, 27, 815, 10.1038/s41591-021-01335-4
DeLong, 1988, Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach, Biometrics, 44, 837, 10.2307/2531595
Savarese, 2017, Global public health burden of heart failure, Card Fail Rev, 3, 7, 10.15420/cfr.2016:25:2
de Couto, 2010, Early detection of myocardial dysfunction and heart failure, Nat Rev Cardiol, 7, 334, 10.1038/nrcardio.2010.51
Cowie, 2017, The heart failure epidemic: a UK perspective, Echo Res Pract, 4, R15, 10.1530/ERP-16-0043
Schwalbe, 2020, Artificial intelligence and the future of global health, Lancet, 395, 1579, 10.1016/S0140-6736(20)30226-9
Celi, 2019, An awakening in medicine: the partnership of humanity and intelligent machines, Lancet Digit Health, 1, e255, 10.1016/S2589-7500(19)30127-X
O'Neal, 2017, Electrocardiographic predictors of heart failure with reduced versus preserved ejection fraction: the multi-ethnic study of atherosclerosis, J Am Heart Assoc, 6, 10.1161/JAHA.117.006023
Hendry, 2016, Scoring system based on electrocardiogram features to predict the type of heart failure in patients with chronic heart failure, Cardiol Res, 7, 110, 10.14740/cr473w
Alhamaydeh, 2020, Identifying the most important ECG predictors of reduced ejection fraction in patients with suspected acute coronary syndrome, J Electrocardiol, 61, 81, 10.1016/j.jelectrocard.2020.06.003
Rim, 2021, Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs, Lancet Digit Health, 3, e306, 10.1016/S2589-7500(21)00043-1
Topol, 2019, High-performance medicine: the convergence of human and artificial intelligence, Nat Med, 25, 44, 10.1038/s41591-018-0300-7
Parikh, 2019, Regulation of predictive analytics in medicine, Science, 363, 810, 10.1126/science.aaw0029
Nagendran, 2020, Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies, BMJ, 368, m689, 10.1136/bmj.m689
Orchard, 2019, Uptake of a primary care atrial fibrillation screening program (AF-SMART): a realist evaluation of implementation in metropolitan and rural general practice, BMC Fam Pract, 20, 170, 10.1186/s12875-019-1058-9
Wahl, 2018, Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings?, BMJ Glob Health, 3, 10.1136/bmjgh-2018-000798
Chorba, 2021, Deep learning algorithm for automated cardiac murmur detection via a digital stethoscope platform, J Am Heart Assoc, 10, 10.1161/JAHA.120.019905
Attia, 2019, An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction, Lancet, 394, 861, 10.1016/S0140-6736(19)31721-0
Hagiwara, 2018, Computer-aided diagnosis of atrial fibrillation based on ECG signals: a review, Inf Sci, 467, 99, 10.1016/j.ins.2018.07.063