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Open Access 01-12-2023 | Artificial Intelligence | Review

Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives

Authors: Xiaoyu Sun, Yuzhe Yin, Qiwei Yang, Tianqi Huo

Published in: European Journal of Medical Research | Issue 1/2023

Abstract

Artificial intelligence (AI), the technique of extracting information from complex database using sophisticated computer algorithms, has incorporated itself in medical field. AI techniques have shown the potential to accelerate the progression of diagnosis and treatment of cardiovascular diseases (CVDs), including heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease and so on. In clinical scenario, AI have been proved to apply well in CVD diagnosis, enhance effectiveness of auxiliary tools, disease stratification and typing, and outcome prediction. Deeply developed to capture subtle connections from massive amounts of healthcare data, recent AI algorithms are expected to handle even more complex tasks than traditional methods. The aim of this review is to introduce current applications of AI in CVDs, which may allow clinicians who have limited expertise of computer science to better understand the frontier of the subject and put AI algorithms into clinical practice.

Xu D, Liu R, Xu H, Zhang Z, Li W, Zhang Y, et al. Adoption of two-dimensional ultrasound gastrointestinal filling contrast on artificial intelligence algorithm in clinical diagnosis of gastric cancer. Comput Math Methods Med. 2022;2022:7385344.PubMedPubMedCentral

Montull L, Slapsinskaite-Dackeviciene A, Kiely J, Hristovski R, Balague N. Integrative proposals of sports monitoring: subjective outperforms objective monitoring. Sports Med Open. 2022;8(1):41.PubMedPubMedCentralCrossRef

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(1):70–4.PubMedCrossRef

Attia ZI, Kapa S, Noseworthy PA, Lopez-Jimenez F, Friedman PA. Artificial intelligence ECG to detect left ventricular dysfunction in COVID-19: a case series. Mayo Clin Proc. 2020;95(11):2464–6.PubMedCrossRef

Yao X, Rushlow DR, Inselman JW, McCoy RG, Thacher TD, Behnken EM, et al. Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nat Med. 2021;27(5):815–9.PubMedCrossRef

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. Lancet. 2019;394(10201):861–7.PubMedCrossRef

Kwon JM, Cho Y, Jeon KH, Cho S, Kim KH, Baek SD, et al. A deep learning algorithm to detect anaemia with ECGs: a retrospective, multicentre study. Lancet Digit Health. 2020;2(7):e358–67.PubMedCrossRef

Ko WY, Siontis KC, Attia ZI, Carter RE, Kapa S, Ommen SR, et al. Detection of hypertrophic cardiomyopathy using a convolutional neural network-enabled electrocardiogram. J Am Coll Cardiol. 2020;75(7):722–33.PubMedCrossRef

Kwon JM, Kim KH, Medina-Inojosa J, Jeon KH, Park J, Oh BH. Artificial intelligence for early prediction of pulmonary hypertension using electrocardiography. J Heart Lung Transplant. 2020;39(8):805–14.PubMedCrossRef

10.

Cho H, Keenan G, Madandola OO, Dos Santos FC, Macieira TGR, Bjarnadottir RI, et al. Assessing the usability of a clinical decision support system: heuristic evaluation. JMIR Hum Factors. 2022;9(2):e31758.PubMedPubMedCentralCrossRef

11.

Emile SH, Hamid HKS. Fighting COVID-19, a place for artificial intelligence. Transbound Emerg Dis. 2020;67(5):1754–5.PubMedPubMedCentral

12.

Dankwa-Mullan I, Rivo M, Sepulveda M, Park Y, Snowdon J, Rhee K. Transforming diabetes care through artificial intelligence: the future is here. Popul Health Manag. 2019;22(3):229–42.PubMedPubMedCentralCrossRef

13.

Yan Y, Zhang JW, Zang GY, Pu J. The primary use of artificial intelligence in cardiovascular diseases: what kind of potential role does artificial intelligence play in future medicine? J Geriatr Cardiol. 2019;16(8):585–91.PubMedPubMedCentral

14.

Goodswen SJ, Barratt JLN, Kennedy PJ, Kaufer A, Calarco L, Ellis JT. Machine learning and applications in microbiology. FEMS Microbiol Rev. 2021;45:5.CrossRef

15.

Zhu R, Jiang C, Wang X, Wang S, Zheng H, Tang H. Privacy-preserving construction of generalized linear mixed model for biomedical computation. Bioinformatics. 2020;36(1):128–35.CrossRef

16.

Yadav RS. Data analysis of COVID-2019 epidemic using machine learning methods: a case study of India. Int J Inf Technol. 2020;12(4):1321–30.PubMedPubMedCentral

17.

Kahr M, Kovacs G, Loinig M, Bruckl H. Condition monitoring of ball bearings based on machine learning with synthetically generated data. Sensors. 2022;22:7.CrossRef

18.

Ali A, Hu B, Ramahi O. Intelligent detection of cracks in metallic surfaces using a waveguide sensor loaded with metamaterial elements. Sensors. 2015;15(5):11402–16.PubMedPubMedCentralCrossRef

19.

Muller E, Arnold E, Breitwieser O, Czierlinski M, Emmel A, Kaiser J, et al. A scalable approach to modeling on accelerated neuromorphic hardware. Front Neurosci. 2022;16:884128.PubMedPubMedCentralCrossRef

20.

Yadav AK, Banerjee SK, Das B, Chaudhary K. Editorial: systems biology and omics approaches for understanding complex disease biology. Front Genet. 2022;13:896818.PubMedPubMedCentralCrossRef

21.

de Mattos Neto PSG, de Oliveira JFL, Bassetto P, Siqueira HV, Barbosa L, Alves EP, et al. Energy consumption forecasting for smart meters using extreme learning machine ensemble. Sensors. 2021;21:23.CrossRef

22.

Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol. 2017;69(21):2657–64.PubMedCrossRef

23.

Chen L, Li N, Zheng Y, Gao L, Ge N, Xie D, et al. A novel semiautomatic Chinese keywords instrument screening delirium based on electronic medical records. BMC Geriatr. 2022;22(1):779.PubMedPubMedCentralCrossRef

24.

Guan SY, Liu YY, Guo Y, Shen XX, Liu Y, Jin HX. Potential biomarkers for clinical outcomes of IVF cycles in women with/without PCOS: searching with metabolomics. Front Endocrinol. 2022;13:982200.CrossRef

25.

Lenstrup M, Kjaergaard J, Petersen CL, Kjaer A, Hassager C. Evaluation of left ventricular mass measured by 3D echocardiography using magnetic resonance imaging as gold standard. Scand J Clin Lab Invest. 2006;66(8):647–57.PubMedCrossRef

26.

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.PubMedCrossRef

27.

Saikrishnan N, Kumar G, Sawaya FJ, Lerakis S, Yoganathan AP. Accurate assessment of aortic stenosis: a review of diagnostic modalities and hemodynamics. Circulation. 2014;129(2):244–53.PubMedCrossRef

28.

Japp AG, Gulati A, Cook SA, Cowie MR, Prasad SK. The diagnosis and evaluation of dilated cardiomyopathy. J Am Coll Cardiol. 2016;67(25):2996–3010.PubMedCrossRef

29.

Shrivastava S, Cohen-Shelly M, Attia ZI, Rosenbaum AN, Wang L, Giudicessi JR, et al. Artificial intelligence-enabled electrocardiography to screen patients with dilated cardiomyopathy. Am J Cardiol. 2021;155:121–7.PubMedCrossRef

30.

Elias P, Poterucha TJ, Rajaram V, Moller LM, Rodriguez V, Bhave S, et al. Deep learning electrocardiographic analysis for detection of left-sided valvular heart disease. J Am Coll Cardiol. 2022;80(6):613–26.PubMedCrossRef

31.

Siontis KC, Noseworthy PA, Attia ZI, Friedman PA. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol. 2021;18(7):465–78.PubMedPubMedCentralCrossRef

32.

Attia ZI, Harmon DM, Behr ER, Friedman PA. Application of artificial intelligence to the electrocardiogram. Eur Heart J. 2021;42(46):4717–30.PubMedCrossRef

33.

Lancellotti P, Magne J, Dulgheru R, Clavel M-A, Donal E, Vannan MA, et al. Outcomes of patients with asymptomatic aortic stenosis followed up in heart valve clinics. JAMA Cardiol. 2018;3(11):1060–8.PubMedPubMedCentralCrossRef

34.

Leon MB, Smith CR, Mack M, Miller DC, Moses JW, Svensson LG, et al. Transcatheter aortic-valve implantation for aortic stenosis in patients who cannot undergo surgery. N Engl J Med. 2010;363(17):1597–607.PubMedCrossRef

35.

Kwon JM, Lee SY, Jeon KH, Lee Y, Kim KH, Park J, et al. Deep learning-based algorithm for detecting aortic stenosis using electrocardiography. J Am Heart Assoc. 2020;9(7):e014717.PubMedPubMedCentralCrossRef

36.

Cohen-Shelly M, Attia ZI, Friedman PA, Ito S, Essayagh BA, Ko W-Y, et al. Electrocardiogram screening for aortic valve stenosis using artificial intelligence. Eur Heart J. 2021;42(30):2885–96.PubMedCrossRef

37.

Siontis KC, Gersh BJ, Killian JM, Noseworthy PA, McCabe P, Weston SA, et al. Typical, atypical, and asymptomatic presentations of new-onset atrial fibrillation in the community: characteristics and prognostic implications. Heart Rhythm. 2016;13(7):1418–24.PubMedPubMedCentralCrossRef

38.

Davidson KW, Barry MJ, Mangione CM, Cabana M, Caughey AB, Davis EM, et al. Screening for atrial fibrillation: US preventive services task force recommendation statement. JAMA. 2022;327(4):360–7.PubMedCrossRef

39.

Khurshid S, Friedman S, Reeder C, Di Achille P, Diamant N, Singh P, et al. ECG-based deep learning and clinical risk factors to predict atrial fibrillation. Circulation. 2022;145(2):122–33.PubMedCrossRef

40.

Noseworthy PA, Attia ZI, Behnken EM, Giblon RE, Bews KA, Liu S, et al. Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial. Lancet. 2022;400(10359):1206–12.PubMedCrossRef

41.

Betancur J, Commandeur F, Motlagh M, Sharir T, Einstein AJ, Bokhari S, et al. Deep learning for prediction of obstructive disease from fast myocardial perfusion SPECT: a multicenter study. JACC Cardiovasc Imaging. 2018;11(11):1654–63.PubMedPubMedCentralCrossRef

42.

Christoffersen M, Tybjærg-Hansen A. Visible aging signs as risk markers for ischemic heart disease: epidemiology, pathogenesis and clinical implications. Ageing Res Rev. 2016;25:24–41.PubMedCrossRef

43.

Lin S, Li Z, Fu B, Chen S, Li X, Wang Y, et al. Feasibility of using deep learning to detect coronary artery disease based on facial photo. Eur Heart J. 2020;41(46):4400–11.PubMedCrossRef

44.

Yan BP, Lai WHS, Chan CKY, Au ACK, Freedman B, Poh YC, et al. High-throughput, contact-free detection of atrial fibrillation from video with deep learning. JAMA Cardiol. 2020;5(1):105–7.PubMedCrossRef

45.

de Couto G, Ouzounian M, Liu PP. Early detection of myocardial dysfunction and heart failure. Nat Rev Cardiol. 2010;7(6):334–44.PubMedCrossRef

46.

Khurshid S, Friedman S, Pirruccello JP, Di Achille P, Diamant N, Anderson CD, et al. Deep learning to predict cardiac magnetic resonance-derived left ventricular mass and hypertrophy from 12-lead ECGs. Circ Cardiovasc Imaging. 2021;14(6):e012281.PubMedPubMedCentralCrossRef

47.

Maron BJ, Haas TS, Murphy CJ, Ahluwalia A, Rutten-Ramos S. Incidence and causes of sudden death in U.S. college athletes. J Am Coll Cardiol. 2014;63(16):1636–43.PubMedCrossRef

48.

McLeod CJ, Ackerman MJ, Nishimura RA, Tajik AJ, Gersh BJ, Ommen SR. Outcome of patients with hypertrophic cardiomyopathy and a normal electrocardiogram. J Am Coll Cardiol. 2009;54(3):229–33.PubMedCrossRef

49.

Liu CM, Chang SL, Chen HH, Chen WS, Lin YJ, Lo LW, et al. The clinical application of the deep learning technique for predicting trigger origins in patients with paroxysmal atrial fibrillation with catheter ablation. Circ Arrhythm Electrophysiol. 2020;13(11):e008518.PubMedCrossRef

50.

Arnaout R, Curran L, Zhao Y, Levine JC, Chinn E, Moon-Grady AJ. An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease. Nat Med. 2021;27(5):882–91.PubMedPubMedCentralCrossRef

51.

Donofrio MT, Moon-Grady AJ, Hornberger LK, Copel JA, Sklansky MS, Abuhamad A, et al. Diagnosis and treatment of fetal cardiac disease: a scientific statement from the American heart association. Circulation. 2014;129(21):2183–242.PubMedCrossRef

52.

Sun HY, Proudfoot JA, McCandless RT. Prenatal detection of critical cardiac outflow tract anomalies remains suboptimal despite revised obstetrical imaging guidelines. Congenit Heart Dis. 2018;13(5):748–56.PubMedPubMedCentralCrossRef

53.

Asch FM, Mor-Avi V, Rubenson D, Goldstein S, Saric M, Mikati I, et al. Deep learning-based automated echocardiographic quantification of left ventricular ejection fraction: a point-of-care solution. Circ Cardiovasc Imaging. 2021;14(6):e012293.PubMedCrossRef

54.

Kusunose K, Abe T, Haga A, Fukuda D, Yamada H, Harada M, et al. A deep learning approach for assessment of regional wall motion abnormality from echocardiographic images. JACC Cardiovasc Imaging. 2020;13(2 Pt 1):374–81.PubMedCrossRef

55.

Choi AD, Marques H, Kumar V, Griffin WF, Rahban H, Karlsberg RP, et al. CT evaluation by artificial intelligence for atherosclerosis, stenosis and vascular morphology (CLARIFY): a multi-center, international study. J Cardiovasc Comput Tomogr. 2021;15(6):470–6.PubMedCrossRef

56.

Lin A, Manral N, McElhinney P, Killekar A, Matsumoto H, Kwiecinski J, et al. Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study. Lancet Digit Health. 2022;4(4):e256–65.PubMedPubMedCentralCrossRef

57.

Knott KD, Seraphim A, Augusto JB, Xue H, Chacko L, Aung N, et al. The prognostic significance of quantitative myocardial perfusion: an artificial intelligence-based approach using perfusion mapping. Circulation. 2020;141(16):1282–91.PubMedPubMedCentral

58.

Hae H, Kang SJ, Kim WJ, Choi SY, Lee JG, Bae Y, et al. Machine learning assessment of myocardial ischemia using angiography: development and retrospective validation. PLoS Med. 2018;15(11):e1002693.PubMedPubMedCentralCrossRef

59.

Cho H, Lee JG, Kang SJ, Kim WJ, Choi SY, Ko J, et al. Angiography-based machine learning for predicting fractional flow reserve in intermediate coronary artery lesions. J Am Heart Assoc. 2019;8(4):e011685.PubMedPubMedCentralCrossRef

60.

Cikes M, Sanchez-Martinez S, Claggett B, Duchateau N, Piella G, Butakoff C, et al. Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy. Eur J Heart Fail. 2019;21(1):74–85.PubMedCrossRef

61.

Karwath A, Bunting KV, Gill SK, Tica O, Pendleton S, Aziz F, et al. Redefining beta-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis. Lancet. 2021;398(10309):1427–35.PubMedPubMedCentralCrossRef

62.

Boriani G, Vitolo M, Diemberger I, Proietti M, Valenti AC, Malavasi VL, et al. Optimizing indices of atrial fibrillation susceptibility and burden to evaluate atrial fibrillation severity, risk and outcomes. Cardiovasc Res. 2021;117(7):1–21.PubMedPubMedCentralCrossRef

63.

Proietti M, Vitolo M, Harrison SL, Lane DA, Fauchier L, Marin F, et al. Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational research programme in AF (EORP-AF) general long-term registry. BMC Med. 2021;19(1):256.PubMedPubMedCentralCrossRef

64.

Howard JP, Cook CM, van de Hoef TP, Meuwissen M, de Waard GA, van Lavieren MA, et al. Artificial intelligence for aortic pressure waveform analysis during coronary angiography: machine learning for patient safety. JACC Cardiovasc Interv. 2019;12(20):2093–101.PubMedCrossRef

65.

Yang DY, Nie ZQ, Liao LZ, Zhang SZ, Zhou HM, Sun XT, et al. Phenomapping of subgroups in hypertensive patients using unsupervised data-driven cluster analysis: an exploratory study of the SPRINT trial. Eur J Prev Cardiol. 2019;26(16):1693–706.PubMedCrossRef

66.

Reel PS, Reel S, van Kralingen JC, Langton K, Lang K, Erlic Z, et al. Machine learning for classification of hypertension subtypes using multi-omics: a multi-centre, retrospective, data-driven study. EBioMedicine. 2022;84:104276.PubMedPubMedCentralCrossRef

67.

Maron BJ, Maron MS, Semsarian C. Genetics of hypertrophic cardiomyopathy after 20 years: clinical perspectives. J Am Coll Cardiol. 2012;60(8):705–15.PubMedCrossRef

68.

Zhou H, Li L, Liu Z, Zhao K, Chen X, Lu M, et al. Deep learning algorithm to improve hypertrophic cardiomyopathy mutation prediction using cardiac cine images. Eur Radiol. 2021;31(6):3931–40.PubMedCrossRef

69.

Raghunath S, Ulloa Cerna AE, Jing L, vanMaanen DP, Stough J, Hartzel DN, et al. Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network. Nat Med. 2020;26(6):886–91.PubMedCrossRef

70.

Toya T, Ahmad A, Attia Z, Cohen-Shelly M, Ozcan I, Noseworthy PA, et al. Vascular aging detected by peripheral endothelial dysfunction is associated with ECG-derived physiological aging. J Am Heart Assoc. 2021;10(3):e018656.PubMedPubMedCentralCrossRef

71.

Cheung CY, Xu D, Cheng CY, Sabanayagam C, Tham YC, Yu M, et al. A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre. Nat Biomed Eng. 2021;5(6):498–508.PubMedCrossRef

72.

de Souza ESCG, Buginga GC, de Souza ESEA, Arena R, Rouleau CR, Aggarwal S, et al. Prediction of mortality in coronary artery disease: role of machine learning and maximal exercise capacity. Mayo Clin Proc. 2022;97(8):1472–82.CrossRef

73.

Backhaus SJ, Aldehayat H, Kowallick JT, Evertz R, Lange T, Kutty S, et al. Artificial intelligence fully automated myocardial strain quantification for risk stratification following acute myocardial infarction. Sci Rep. 2022;12(1):12220.PubMedPubMedCentralCrossRef

74.

Zeleznik R, Foldyna B, Eslami P, Weiss J, Alexander I, Taron J, et al. Deep convolutional neural networks to predict cardiovascular risk from computed tomography. Nat Commun. 2021;12(1):715.PubMedPubMedCentralCrossRef

75.

Eisenberg E, McElhinney PA, Commandeur F, Chen X, Cadet S, Goeller M, et al. Deep learning-based quantification of epicardial adipose tissue volume and attenuation predicts major adverse cardiovascular events in asymptomatic subjects. Circ Cardiovasc Imaging. 2020;13(2):e009829.PubMedPubMedCentralCrossRef

76.

Min HS, Ryu D, Kang SJ, Lee JG, Yoo JH, Cho H, et al. Prediction of coronary stent underexpansion by pre-procedural intravascular ultrasound-based deep learning. JACC Cardiovasc Interv. 2021;14(9):1021–9.PubMedCrossRef

77.

Goldstein BA, Navar AM, Carter RE. Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges. Eur Heart J. 2017;38(23):1805–14.PubMed

78.

Camm AJ, Accetta G, Ambrosio G, Atar D, Bassand JP, Berge E, et al. Evolving antithrombotic treatment patterns for patients with newly diagnosed atrial fibrillation. Heart. 2017;103(4):307–14.PubMedCrossRef

79.

Goto S, Goto S, Pieper KS, Bassand JP, Camm AJ, Fitzmaurice DA, et al. New artificial intelligence prediction model using serial prothrombin time international normalized ratio measurements in atrial fibrillation patients on vitamin K antagonists: GARFIELD-AF. Eur Heart J Cardiovasc Pharmacother. 2020;6(5):301–9.PubMedCrossRef

80.

Kilic A, Goyal A, Miller JK, Gleason TG, Dubrawksi A. Performance of a machine learning algorithm in predicting outcomes of aortic valve replacement. Ann Thorac Surg. 2021;111(2):503–10.PubMedCrossRef

81.

Sherman E, Alejo D, Wood-Doughty Z, Sussman M, Schena S, Ong CS, et al. Leveraging machine learning to predict 30-day hospital readmission after cardiac surgery. Ann Thorac Surg. 2022;114(6):2173–9.PubMedCrossRef

Title: Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives
Authors: Xiaoyu Sun
Yuzhe Yin
Qiwei Yang
Tianqi Huo
Publication date: 01-12-2023
Publisher: BioMed Central
Keywords: Artificial Intelligence
Atrial Fibrillation
Published in: European Journal of Medical Research / Issue 1/2023
Electronic ISSN: 2047-783X
DOI: https://doi.org/10.1186/s40001-023-01065-y

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Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives

Abstract

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Abstract

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