ABC | Volume 114, Nº4, April 2020

Marques et al. Artificial Intelligence in Cardiology Arq Bras Cardiol. 2020; 114(4):718-725 Review Article 1. Goodfellow I, Bengio Y, Courville A. Deep Learning. Cambridge: MIT Press; 2016. 2. DilsizianSE,Siegel,EL.Artificial intelligence inmedicineandcardiac imaging: harnessingbigdataandadvancedcomputingtoprovidepersonalizedmedical diagnosis and treatment. Curr Cardiol Rep. 2014;16(1):441. 3. Michel JB, Sangha DM, Erwin JP 3rd. Burnout among cardiologists. Am J Cardiol. 2017;119(6):938-40. 4. Rotenstein LS, Torre M, Ramos MA, Rosales RC, Guille C, Sen S, et al. Prevalence of burnout among physicians: a systematic review. JAMA. 2018;320(11):1131-50. 5. Macary MA, Daniel M. Medical error – the third leading cause of death in the US. BMJ. 2016 May 3;353:i2139. 6. Porter ME. A strategy for health care reform – toward a value-based system. N Engl J Med. 2009;361(2):109-12. 7. DilsizianME,SiegelEL.Machinemeetsbiology:aprimeronartificialintelligence in cardiology and cardiac imaging. Curr Cardiol Rep. 2018;20(12):139. 8. Johnson KW, Soto JT, Glicksberg BS, Shameer K, Miotto R, Ali M, et al. Artificial Intelligence inCardiology. JAmCollCardiol.2018;71(23):2668-79. 9. Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial intelligence in precisioncardiovascularmedicine. JAmCollCardiol.2017;69(21):2657-64. 10. Massalha S, Clarkin O, Thornhill R, Wells G, Chow BJW. Decision support tools, systems, and artificial intelligence in cardiac imaging. Can J Cardiol. 2018;34(7):827-38. 11. Mesquita CT. Artificial intelligence and machine learning in cardiology – a change of paradigm. Int. J. Cardiovasc. Sci. 2017;30(3):187-8. 12. Moore J.TheDartmouthCollegeArtificial IntelligenceConference:TheNext Fifty Years. AI Magazine. 2006;27(4):87-91. 13. Turing AM. Computing machinery and intelligence. Mind. 1950;59(236):433-60. 14. Price, WN. Big data and black-box medical algorithms. Sci Transl Med. 2018;10(471):pi:eaao5333. 15. Antman EM, Loscalzo J . Precision medicine in cardiology. Nat Rev Cardiol. 2016;13(10):591-602. 16. Price,WN II.BlackBoxmedicine.Harvard JLawTech.2015;28(2):419-467. 17. Dey D , Slomka PJ, Leeson P , Comaniciu D, Shrestha S, Sengupta PP , et al. Artificial intelligence in cardiovascular imaging: JACC State-of-the-Art review. J Am Coll Cardiol. 2019;73(11):1317-35. 18. Shameer K, Johnson KW, Glicksberg BS, Dudley JT, Sengupta PP. Machine learning in cardiovascular medicine: are we there yet? Heart. 2018;104(14):1156-64. 19. Al’Aref SJ, Anchouche K, SinghG, Slomka PJ, Kolli KK, Kumar A, et al. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. Eur Heart J. 2018;40(24):1975-86. 20. Somashekhar SP, Sepulveda MJ, Puglielli S, Norden AD, Shortliffe EH, Rohit Kumar C, et al. Watson for Oncology and breast cancer treatment recommendations:agreementwithanexpertmultidisciplinarytumorboard. Ann Oncol. 2018;29(2):418-23. 21. IBM. Watson. NewYork: IBM; 2019 [Cited in 2018Oct 10]. Available from: https://www.ibm.com/watson/index.html. 22. Samad MD, Wehner GJ , Arbabshirani MR, Jing L, Powell AJ , Geva T , et al. Predicting deterioration of ventricular function in patients with repaired tetralogy of Fallot using machine learning. Eur Heart J Cardiovasc Imaging. 2018;19(7):730-8. 23. Berikol GB, Yildiz O, Özcan IT . Diagnosis of acute coronary syndrome with a support vector machine. J Med Syst. 2016;40(4):84. 24. Betancur J, Rubeaux M, Fuchs TA, Otaki Y, Arnson Y, Slipczuk L, et al. Automatic valve plane localization in myocardial perfusion SPECT/ CT by machine learning: Anatomic and clinical validation. J Nucl Med. 2016;58(6):961-7. 25. ParedesS, RochaT , deCarvalhoP , Henriques J , Morais J , Ferreira J. Integration of different risk assessment tools to improve stratification of patients with coronary artery disease. Med Biol Eng Comput. 2015;53(10):1069-83. 26. Al-MallahMH, Elshawi R, Ahmed AM, Qureshi WT , Brawner CA, BlahaMJ , et al. Using machine learning to the association between cardiorespiratory fitness and all-causemortality (from theHenry Ford Exercise Testing Project). Am J Cardiol. 2017;120(11):2078-84. 27. Smisek R, Hejc J, Ronzhina M, Nemcova A, Marsanova L, Kolarova J, et al. Multi-stage SVM approach for cardiac arrhythmias detection in short single-lead ECG recorded by a wearable device. Physiol Meas. 2018;39(9):094003. 28. Stuckey TD, Gammon RS, Goswami R, Depta JP, Steuter JA, Meine FJ 3rd, et al. Cardiac phase space tomography: a novel method of assessing coronary artery disease utilizingmachine learning. PLoSOne. 2018;13(8):e0198603. 29. Samad MD, Ulloa A, Wehner GJ, Jing L, Hartzel D, Good CW, et al. Predicting survival from large echocardiography and electronic health record datasets: optimization with machine learning. JACC Cardiovasc Imaging. 2019;12(4):681-9. 30. Ambale-Venkatesh B, Yang X, Wu CO, Liu K, Hundley WG, McClelland R, et al. Cardiovascular event prediction bymachine learning: themulti-ethnic study of atherosclerosis. Circ Res.2017;121(9):1092-101. 31. IshwaranH, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. Ann Appl Stat. 2008;2(3):841-60. 32. CikesM,Sanchez-MartinezS,ClaggettB,DuchateauN,PiellaG,ButakoffC,et al.Machinelearning-basedphenogroupinginheartfailuretoidentifyresponders to cardiac resynchronization therapy. Eur J Heart Fail. 2019;21(1):74-85. 33. Kwon JM, Lee Y , Lee Y , Lee S, Park J. An algorithm based on deep learning for predictingin-hospitalcardiacarrest.AmHeartAssoc.2 018;7(13):pii:e008678. 34. Rubin J, Parvaneh S, Rahman A, Conroy B, Babaeizadeh S. Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings. J Electrocardiol. 2018;51(6S):S18-S21. 35. Zhang J, Gajjala S, Agrawal P , Tison GH, Hallock LA, Beussink-Nelson L, et al. Fully automated echocardiogram interpretation in clinical practice. Circulation. 2018;138(16):1623-35. 36. Nakajima K, Kudo T, Nakata T, Kiso K, Kasai T, Taniguchi Y, et al. Diagnostic accuracyofanartificialneuralnetworkcomparedwithstatisticalquantitation of myocardial perfusion images: a Japanese multicenter study. Eur J Nucl Med Mol Imaging. 2017;44(13):2280-9. 37. Mortazavi BJ, Bucholz EM, Desai NR, Huang C, Curtis JP , Masoudi FA, et al. Comparison of machine learningmethods with national cardiovascular data registrymodels for prediction of risk of bleeding after percutaneous coronary intervention. JAMA NetwOpen. 2019;2(7):e196835. 38. Hernesniemi JA, Mahdiani S, Tynkkynen JA, Lyytikäinen LP , Mishra PP , Lehtimäki T , et al. Extensive phenotype data and machine learning in prediction of mortality in acute coronary syndrome - the MADDEC study. Ann Med. 2019;51(2):156-63. 39. NannenV. The Paradox of Overfitting Volker Nannen. [thesis] . Países Baixos: Faculty of Artificial Intelligence at the University of Groningen; 2003. 40. Cortes C,VapnikV.Support-vector networks.MachLearn.1995;20:273-97. References 724

RkJQdWJsaXNoZXIy MjM4Mjg=