Application of artificial intelligence and machine learning in cardiovascular diseases

Abstract

Abstract: Artificial intelligence(AI) and machine learning(ML) have gradually been widely used in the medical field due to their unique advantages. In cardiovascular diseases, this technology has made some progress for processing complex data from electronic medical record, analyzing disease mechanisms and for predicting prognosis, automatically analyzing and identifying cardiovascular imaging and arrhythmia, and discovering new subtypes of disease. The potential of AI and ML in cardiovascular disease research will bring a new breakthrough in the cardiovascular field.

Key words: artificial intelligence, machine learning, cardiovascular disease

CLC Number:

R714.252

SONG Qi-rui, CAI Jun. Application of artificial intelligence and machine learning in cardiovascular diseases[J]. Basic & Clinical Medicine, 2020, 40(5): 707-710.

References

[1]Johnson KW, Torres Soto J, Glicksberg BS, et al.Artificial intelligence in cardiology[J].J Am Coll Cardiol,2018,71:2668-2679.
[2]Shameer K, Johnson KW, Glicksberg BS, et al.Machine learning in cardiovascular medicine: are we there yet?[J].Heart,2018,104:1156-1164.
[3]Sengupta PP, Shrestha S.Machine learning for data-driven discovery: the rise and relevance[J].JACC Cardiovasc Imaging,2019,12:690-692.
[4]Lancaster MC, Salem Omar AM, Narula S, et al.Phenotypic clustering of left ventricular diastolic function parameters: patterns and prognostic relevance[J].JACC Cardiovasc Imaging,2019,12:1149-1161.
[5]Gandhi S, Mosleh W, Shen J, et al.Automation, machine learning, and artificial intelligence in echocardiography: A brave new world[J].Echocardiography,2018,35:1402-1418.
[6]Balakumar P, Maung UK,Jagadeesh G.Prevalence and prevention of cardiovascular disease and diabetes mellitus[J].Pharmacol Res,2016,113:600-609.
[7]Johnson KW, Shameer K, Glicksberg BS, et al.Enabling precision cardiology through multiscale biology and systems medicine[J].JACC Basic Transl Sci,2017,2:311-327.
[8]Antman EM,Loscalzo J.Precision medicine in cardiology[J].Nat Rev Cardiol,2016,13:591-602.
[9]LeCun YBY, Hinton G.Deep learning[J].Nature,2015,521:436-444.
[10]Mallya SOM, Srivastava N, Arai T,et al.Effectiveness of lstms in predicting congestive heart failure onset[EB/OL].Available at: https://arxiv.org/abs/1902.02443. Accessed August 11, 2019.
[11]Diller GP, Kempny A, Babu-Narayan SV, et al.Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients[J].Eur Heart J,2019,40:1069-1077.
[12]Betancur J, Commandeur F, Motlagh M, et al.Deep learning for prediction of obstructive disease from fast myocardial perfusion SPECT: a multicenter study[J].JACC Cardiovasc Imaging,2018,11:1654-1663.
[13]Motwani M, Dey D, Berman DS, et al.Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis[J].Eur Heart J,2017,38:500-507.
[14]Allyn J, Allou N, Augustin P, et al.A comparison of a machine learning model with euroSCORE II in predicting mortality after elective cardiac surgery: a decision curve analysis[J].PLoS One,2017,12.doi: 10.1371/journal.pone.0169772.
[15]Bai W, Sinclair M, Tarroni G, et al.Automated cardiovascular magnetic resonance image analysis with fully convolutional networks[J].J Cardiovasc Magn Reson,2018,20:65.
[16]Zhang J, Gajjala S, Agrawal P, et al. Fully automated echocardiogram interpretation in clinical practice[J].Circulation,2018,138:1623-1635.
[17]Zreik M, Lessmann N, van Hamersvelt RW, et al.Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis[J].Med Image Anal,2018,44:72-85.
[18]Attia ZI, Noseworthy PA, Lopez-Jimenez F, 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[J].Lancet,2019,394:861-867.
[19]Tison GH, Sanchez JM, Ballinger B, et al.Passive detection of atrial fibrillation using a commercially available smartwatch[J].JAMA Cardiol,2018,3:409-416.
[20]Shah SJ, Katz DH, Selvaraj S, et al.Phenomapping for novel classification of heart failure with preserved ejection fraction[J].Circulation,2015,131:269-279.
[21]Yang DY, Nie ZQ, Liao LZ, et al.Phenomapping of subgroups in hypertensive patients using unsupervised data-driven cluster analysis: An exploratory study of the SPRINT trial[J].Eur J Prev Cardiol,2019,26:1693-1706.