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Attia ZI, Friedman PA, Noseworthy PA, Lopez-Jimenez F, Ladewig DJ, Satam G, et al. Age and sex estimation using artificial intelligence from standard 12-lead ECGs. <i>Circ Arrhythm Electrophysiol<\/i> 2019; 12: e007284.","DOI":"10.1161\/CIRCEP.119.007284"},{"key":"2","doi-asserted-by":"crossref","unstructured":"2. Al-Khatib SM, Stevenson WG, Ackerman MJ, Bryant WJ, Callans DJ, Curtis AB, et al. 2017 AHA\/ACC\/HRS guideline for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death. <i>Circulation<\/i> 2018; 138: e272\u2013e391.","DOI":"10.1161\/CIR.0000000000000549"},{"key":"3","doi-asserted-by":"crossref","unstructured":"3. Hancock EW, Deal BJ, Mirvis DM, Okin P, Kligfield P, Gettes LS, et al. AHA\/ACCF\/HRS Recommendations for the standardization and interpretation of the electrocardiogram. Part V: Electrocardiogram changes associated with cardiac chamber hypertrophy: A scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society: Endorsed by the International Society for Computerized Electrocardiology. <i>Circulation<\/i> 2009; 119: e251\u2013e261.","DOI":"10.1161\/CIRCULATIONAHA.108.191097"},{"key":"4","doi-asserted-by":"crossref","unstructured":"4. Rautaharju PM, Surawicz B, Gettes LS, Bailey JJ, Childers R, Deal BJ, et al. AHA\/ACCF\/HRS recommendations for the standardization and interpretation of the electrocardiogram. Part IV: The ST segment, T and U waves, and the QT interval: A scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society endorsed by the International Society for Computerized Electrocardiology. <i>J Am Coll Cardiol<\/i> 2009; 53: 982\u2013991.","DOI":"10.1016\/j.jacc.2008.12.014"},{"key":"5","doi-asserted-by":"crossref","unstructured":"5. Amsterdam EA, Wenger NK, Brindis RG, Casey DE, Ganiats TG, Holmes DR, et al. 2014 AHA\/ACC guideline for the management of patients with non-ST-elevation acute coronary syndromes. A report of the American College of Cardiology\/American Heart Association Task Force on Practice Guidelines. <i>J Am Coll Cardiol<\/i> 2014; 64: e139\u2013e228.","DOI":"10.1161\/CIR.0000000000000133"},{"key":"6","doi-asserted-by":"crossref","unstructured":"6. Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. <i>Nat Med<\/i> 2019; 25: 44\u201356.","DOI":"10.1038\/s41591-018-0300-7"},{"key":"7","doi-asserted-by":"crossref","unstructured":"7. Zhang J, Gajjala S, Agrawal P, Tison GH, Hallock LA, Beussink-Nelson L, et al. Fully automated echocardiogram interpretation in clinical practice. <i>Circulation<\/i> 2018; 138: 1623\u20131635.","DOI":"10.1161\/CIRCULATIONAHA.118.034338"},{"key":"8","doi-asserted-by":"crossref","unstructured":"8. Goto S, Kimura M, Katsumata Y, Goto S, Kamatani T, Ichihara G, et al. Artificial intelligence to predict needs for urgent revascularization from 12-lead electrocardiography in emergency patients. <i>PLoS One<\/i> 2019; 14: e0210103.","DOI":"10.1371\/journal.pone.0210103"},{"key":"9","doi-asserted-by":"crossref","unstructured":"9. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. <i>In<\/i>: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016: 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10","doi-asserted-by":"crossref","unstructured":"10. Kim JH, Kwon HS, Seo HW. Evaluating a pivot-based approach for bilingual lexicon extraction. <i>Comput Intell Neurosci<\/i> 2015; 2015: 13.","DOI":"10.1155\/2015\/434153"},{"key":"11","doi-asserted-by":"crossref","unstructured":"11. Pasquier M, Quek C, Toh M. Fuzzylot: A novel self-organising fuzzy-neural rule-based pilot system for automated vehicles. <i>Neural Netw<\/i> 2001; 14: 1099\u20131112.","DOI":"10.1016\/S0893-6080(01)00048-X"},{"key":"12","unstructured":"12. Ribeiro AL, de Oliveira GMM. Toward a patient-centered, data-driven cardiology. <i>Arq Bras Cardiol<\/i> 2019; 112: 371\u2013373."},{"key":"13","doi-asserted-by":"crossref","unstructured":"13. Goto S, Goto S. Letter by Goto and Goto regarding article, \u201cFully automated echocardiogram interpretation in clinical practice: Feasibility and diagnostic accuracy\u201d. <i>Circulation<\/i> 2019; 139: 1646\u20131647.","DOI":"10.1161\/CIRCULATIONAHA.118.038451"},{"key":"14","doi-asserted-by":"crossref","unstructured":"14. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. <i>Nature<\/i> 2017; 542: 115.","DOI":"10.1038\/nature21056"},{"key":"15","doi-asserted-by":"crossref","unstructured":"15. Yoshida H, Shimazu T, Kiyuna T, Marugame A, Yamashita Y, Cosatto E, et al. Automated histological classification of whole-slide images of gastric biopsy specimens. <i>Gastric Cancer<\/i> 2018; 21: 249\u2013257.","DOI":"10.1007\/s10120-017-0731-8"},{"key":"16","doi-asserted-by":"crossref","unstructured":"16. Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, et al. Scalable and accurate deep learning with electronic health records. <i>NPJ Digit Med<\/i> 2018; 1: 18.","DOI":"10.1038\/s41746-018-0029-1"},{"key":"17","doi-asserted-by":"crossref","unstructured":"17. Toma\u0161ev N, Glorot X, Rae JW, Zielinski M, Askham H, Saraiva A, et al. A clinically applicable approach to continuous prediction of future acute kidney injury. <i>Nature<\/i> 2019; 572: 116\u2013119.","DOI":"10.1038\/s41586-019-1390-1"},{"key":"18","doi-asserted-by":"crossref","unstructured":"18. Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: A retrospective analysis of outcome prediction. <i>Lancet<\/i> 2019; 394: 861\u2013867.","DOI":"10.1016\/S0140-6736(19)31721-0"},{"key":"19","doi-asserted-by":"crossref","unstructured":"19. Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. <i>Nat Med<\/i> 2019; 25: 65\u201369.","DOI":"10.1038\/s41591-018-0268-3"},{"key":"20","doi-asserted-by":"crossref","unstructured":"20. 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. <i>Nat Med<\/i> 2019; 25: 70\u201374.","DOI":"10.1038\/s41591-018-0240-2"},{"key":"21","doi-asserted-by":"crossref","unstructured":"21. Yang T, Yu L, Jin Q, Wu L, He B. Localization of origins of premature ventricular contraction by means of convolutional neural network from 12-lead ECG. <i>IEEE Trans Biomed Eng<\/i> 2018; 65: 1662\u20131671.","DOI":"10.1109\/TBME.2017.2756869"},{"key":"22","doi-asserted-by":"crossref","unstructured":"22. Nugent CD, Webb JA, Black ND, Wright GT, McIntyre M. An intelligent framework for the classification of the 12-lead ECG. <i>Artif Intell Med<\/i> 1999; 16: 205\u2013222.","DOI":"10.1016\/S0933-3657(99)00006-8"},{"key":"23","doi-asserted-by":"crossref","unstructured":"23. Heden B, Ohlsson M, Holst H, Mjoman M, Rittner R, Pahlm O, et al. Detection of frequently overlooked electrocardiographic lead reversals using artificial neural networks. <i>Am J Cardiol<\/i> 1996; 78: 600\u2013604.","DOI":"10.1016\/S0002-9149(96)00377-3"},{"key":"24","doi-asserted-by":"crossref","unstructured":"24. Haraldsson H, Edenbrandt L, Ohlsson M. Detecting acute myocardial infarction in the 12-lead ECG using Hermite expansions and neural networks. <i>Artif Intell Med<\/i> 2004; 32: 127\u2013136.","DOI":"10.1016\/j.artmed.2004.01.003"},{"key":"25","unstructured":"25. Heden B, Ohlsson M, Rittner R, Pahlm O, Haisty WK Jr, Peterson C, et al. Agreement between artificial neural networks and experienced electrocardiographer on electrocardiographic diagnosis of healed myocardial infarction. <i>J Am Coll Cardiol<\/i> 1996; 28: 1012\u20131016."},{"key":"26","doi-asserted-by":"crossref","unstructured":"26. Tison GH, Zhang J, Delling FN, Deo RC. Automated and interpretable patient ECG profiles for disease detection, tracking, and discovery. <i>Circ Cardiovasc Qual Outcomes<\/i> 2019; 12: e005289.","DOI":"10.1161\/CIRCOUTCOMES.118.005289"},{"key":"27","doi-asserted-by":"crossref","unstructured":"27. Ohlsson M, Ohlin H, Wallerstedt SM, Edenbrandt L. Usefulness of serial electrocardiograms for diagnosis of acute myocardial infarction. <i>Am J Cardiol<\/i> 2001; 88: 478\u2013481.","DOI":"10.1016\/S0002-9149(01)01722-2"},{"key":"28","doi-asserted-by":"crossref","unstructured":"28. Yang S, Yamauchi K, Nonokawa M, Ikeda M. Use of an artificial neural network to differentiate between ECGs with IRBBB patterns of atrial septal defect and healthy subjects. <i>Med Inform Internet Med<\/i> 2002; 27: 49\u201358.","DOI":"10.1080\/14639230210124444"},{"key":"29","doi-asserted-by":"crossref","unstructured":"29. Nugent CD, Webb JA, Black ND. Feature and classifier fusion for 12-lead ECG classification. <i>Med Inform Internet Med<\/i> 2000; 25: 225\u2013235.","DOI":"10.1080\/146392300750019217"},{"key":"30","doi-asserted-by":"crossref","unstructured":"31. Javadi M, Ebrahimpour R, Sajedin A, Faridi S, Zakernejad S. Improving ECG classification accuracy using an ensemble of neural network modules. <i>PLoS One<\/i> 2011; 6: e24386.","DOI":"10.1371\/journal.pone.0024386"},{"key":"31","doi-asserted-by":"crossref","unstructured":"32. Heden B, Ohlin H, Rittner R, Edenbrandt L. Acute myocardial infarction detected in the 12-lead ECG by artificial neural networks. <i>Circulation<\/i> 1997; 96: 1798\u20131802.","DOI":"10.1161\/01.CIR.96.6.1798"},{"key":"32","doi-asserted-by":"crossref","unstructured":"33. Heden B, Edenbrandt L, Haisty WK Jr, Pahlm O. Artificial neural networks for the electrocardiographic diagnosis of healed myocardial infarction. <i>Am J Cardiol<\/i> 1994; 74: 5\u20138.","DOI":"10.1016\/0002-9149(94)90482-0"},{"key":"33","unstructured":"34. Goodfellow IJ, Shlens J, Szegedy C. Explaining and harnessing adversarial examples. 2014. arXiv:1412.6572."},{"key":"34","doi-asserted-by":"crossref","unstructured":"35. Edenbrandt L, Devine B, Macfarlane PW. Classification of electrocardiographic ST-T segments: Human expert vs artificial neural network. <i>Eur Heart J<\/i> 1993; 14: 464\u2013468.","DOI":"10.1093\/eurheartj\/14.4.464"},{"key":"35","doi-asserted-by":"crossref","unstructured":"36. Reek S, Burri H, Roberts PR, Perings C, Epstein AE, Klein HU, et al. The wearable cardioverter-defibrillator: Current technology and evolving indications. <i>Europace<\/i> 2017; 19: 335\u2013345.","DOI":"10.1093\/europace\/euw180"},{"key":"36","doi-asserted-by":"crossref","unstructured":"37. Lauschke J, Busch M, Haverkamp W, Bulava A, Schneider R, Andresen D, et al. New implantable cardiac monitor with three-lead ECG and active noise detection. <i>Herz<\/i> 2017; 42: 585\u2013592.","DOI":"10.1007\/s00059-016-4492-7"},{"key":"37","doi-asserted-by":"crossref","unstructured":"38. Friedman DJ, Parzynski CS, Heist EK, Russo AM, Akar JG, Freeman JV, et al. Ventricular fibrillation conversion testing after implantation of a subcutaneous implantable cardioverter defibrillator: Report from the National Cardiovascular Data Registry. <i>Circulation<\/i> 2018; 137: 2463\u20132477.","DOI":"10.1161\/CIRCULATIONAHA.117.032167"},{"key":"38","doi-asserted-by":"crossref","unstructured":"39. Kober L, Thune JJ, Nielsen JC, Haarbo J, Videbaek L, Korup E, et al. Defibrillator implantation in patients with nonischemic systolic heart failure. <i>N Engl J Med<\/i> 2016; 375: 1221\u20131230.","DOI":"10.1056\/NEJMoa1608029"},{"key":"39","doi-asserted-by":"crossref","unstructured":"40. Rojo-Alvarez JL, Arenal A, Garcia-Alberola A, Ortiz M, Valdes M, Artes-Rodriguez A. 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