Application of artificial intelligence in cardiovascular medicine_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

QIU Hailong ^1,2 , GUO Huiming ^1,2 , YAO Zeyang ^1,2 , XIE Wen ^1,2 , XU Xiaowei ² , HUANG Meiping ² , CEN Jianzheng ^1,2 ,  ZHUANG Jian ^1,2

1. Department of Cardiovascular Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, P.R.China;
2. Laboratory of Artificial Intelligence and 3D Technologies for Cardiovascular Diseases, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, P.R.China;

Corresponding author：

ZHUANG Jian, Email: Zhuangjian5413@163.com

Keywords：

Artificial intelligence; cardiovascular disease; machine learning; prediction model; review

DOI：

10.7507/1007-4848.202103107

Video：

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Cardiovascular diseases are the leading cause of death and their diagnosis and treatment rely heavily on the variety of clinical data. With the advent of the era of medical big data, artificial intelligence (AI) has been widely applied in many aspects such as imaging, diagnosis and prognosis prediction in cardiovascular medicine, providing a new method for accurate diagnosis and treatment. This paper reviews the application of AI in cardiovascular medicine.

Citation： QIU Hailong, GUO Huiming, YAO Zeyang, XIE Wen, XU Xiaowei, HUANG Meiping, CEN Jianzheng, ZHUANG Jian. Application of artificial intelligence in cardiovascular medicine. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2021, 28(10): 1160-1166. doi: 10.7507/1007-4848.202103107 Copy

1.	Sacco RL, Roth GA, Reddy KS, et al. The heart of 25 by 25: Achieving the goal of reducing global and regional premature deaths from cardiovascular diseases and stroke: A modeling study from the American Heart Association and World Heart Federation. Circulation, 2016, 133(23): e674-e690.
2.	Zhou M, Wang H, Zhu J, et al. Cause-specific mortality for 240 causes in China during 1990-2013: A systematic subnational analysis for the Global Burden of Disease Study 2013. Lancet, 2016, 387(10015): 251-272.
3.	Goff DC, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol, 2014, 63(25 Pt B): 2935-2959.
4.	Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Predicting cardiovascular risk in England and Wales: Prospective derivation and validation of QRISK2. BMJ, 2008, 336(7659): 1475-1482.
5.	D'Agostino RB, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: The Framingham Heart Study. Circulation, 2008, 117(6): 743-753.
6.	Ridker PM, Buring JE, Rifai N, et al. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: The Reynolds Risk Score. JAMA, 2007, 297(6): 611-619.
7.	Alsharqi M, Woodward WJ, Mumith JA, et al. Artificial intelligence and echocardiography. Echo Res Pract, 2018, 5(4): R115-R125.
8.	Gao Xiaohong, Li Wei, Loomes Martin, et al. A fused deep learning architecture for viewpoint classification of echocardiography. Inform Fusion, 2016, 36: 103-113.
9.	Blansit K, Retson T, Masutani E, et al. Deep learning-based prescription of cardiac MRI planes. Radiol Artif Intell, 2019, 1(6): e180069.
10.	Carmel H, Devos D, Lu XG. Fully automatic planning of the long-axis views of the heart. J Cardiovasc Magn Reson, 2013, 15(Suppl 1): O54.
11.	Lorch B, Vaillant G, Baumgartner C, et al. Automated detection of motion artefacts in MR imaging using decision forests. J Med Eng, 2017, 2017: 4501647.
12.	Hauptmann A, Arridge S, Lucka F, et al. Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning-proof of concept in congenital heart disease. Magn Reson Med, 2019, 81(2): 1143-1156.
13.	Leclerc S, Grenier T, Espinosa F, et al. A fully automatic and multi-structural segmentation of the left ventricle and the myocardium on highly heterogeneous 2D echocardiographic data. Washington: 2017 IEEE International Ultrasonics Symposium (IUS).
14.	Leclerc S, Smistad E, Pedrosa J, et al. Deep learning for segmentation using an open large-scale dataset in 2D echocardiography. IEEE Trans Med Imaging, 2019, 38(9): 2198-2210.
15.	Suinesiaputra A, Bluemke DA, Cowan BR, et al. Quantification of LV function and mass by cardiovascular magnetic resonance: Multi-center variability and consensus contours. J Cardiovasc Magn Reson, 2015, 17(1): 63.
16.	Bernard O, Lalande A, Zotti C, et al. Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved? IEEE Trans Med Imaging, 2018, 37(11): 2514-2525.
17.	Bai W, Sinclair M, Tarroni G, et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. J Cardiovasc Magn Reson, 2018, 20(1): 65.
18.	Oktay O, Ferrante E, Kamnitsas K, et al. Anatomically Constrained Neural Networks (ACNNs): Application to cardiac image enhancement and segmentation. IEEE Trans Med Imaging, 2018, 37(2): 384-395.
19.	Zheng Y, Barbu A, Georgescu B, et al. Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans Med Imaging, 2008, 27(11): 1668-1681.
20.	Gurpreet S, Subhi A, Gabriel M, et al. Deep learning based automatic segmentation of cardiac computed tomography. J Am Coll Cardiol, 2019, 73(Suppl 9): 1643.
21.	Cannesson M, Tanabe M, Suffoletto MS, et al. A novel two-dimensional echocardiographic image analysis system using artificial intelligence-learned pattern recognition for rapid automated ejection fraction. J Am Coll Cardiol, 2007, 49(2): 217-226.
22.	Madani A, Ong JR, Tibrewal A, et al. Deep echocardiography: Data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease. NPJ Digit Med, 2018, 1: 59.
23.	Sanchez-Martinez S, Duchateau N, Erdei T, et al. Characterization of myocardial motion patterns by unsupervised multiple kernel learning. Med Image Anal, 2017, 35: 70-82.
24.	Knackstedt C, Bekkers SC, Schummers G, et al. Fully automated versus standard tracking of left ventricular ejection fraction and longitudinal strain: The FAST-EFs multicenter study. J Am Coll Cardiol, 2015, 66(13): 1456-1466.
25.	Lang RM, Badano LP, Mor-Avi V, et al. Recommendations for cardiac chamber quantification by echocardiography in adults: An update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J Am Soc Echocardiogr, 2015, 28(1): 1-39.
26.	Narang A, Mor-Avi V, Prado A, et al. Machine learning based automated dynamic quantification of left heart chamber volumes. Eur Heart J Cardiovasc Imaging, 2019, 20(5): 541-549.
27.	Tsang W, Salgo IS, Medvedofsky D, et al. Transthoracic 3D Echocardiographic left heart chamber quantification using an automated adaptive analytics algorithm. JACC Cardiovasc Imaging, 2016, 9(7): 769-782.
28.	Volpato V, Mor-Avi V, Narang A, et al. Automated, machine learning-based, 3D echocardiographic quantification of left ventricular mass. Echocardiography, 2019, 36(2): 312-319.
29.	Genovese D, Rashedi N, Weinert L, et al. Machine learning-based three-dimensional echocardiographic quantification of right ventricular size and function: Validation against cardiac magnetic resonance. J Am Soc Echocardiogr, 2019, 32(8): 969-977.
30.	Suinesiaputra A, Sanghvi MM, Aung N, et al. Fully-automated left ventricular mass and volume MRI analysis in the UK Biobank population cohort: Evaluation of initial results. Int J Cardiovasc Imaging, 2018, 34(2): 281-291.
31.	Ngo TA, Lu Z, Carneiro G. Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance. Med Image Anal, 2017, 35: 159-171.
32.	Marino M, Veronesi F, Corsi C. Fully automated assessment of left ventricular volumes and mass from cardiac magnetic resonance images. Annu Int Conf IEEE Eng Med Biol Soc, 2014, 2014: 1079-1082.
33.	Avendi MR, Kheradvar A, Jafarkhani H. Automatic segmentation of the right ventricle from cardiac MRI using a learning-based approach. Magn Reson Med, 2017, 78(6): 2439-2448.
34.	Petitjean C, Zuluaga MA, Bai W, et al. Right ventricle segmentation from cardiac MRI: A collation study. Med Image Anal, 2015, 19(1): 187-202.
35.	Zabihollahy F, White JA, Ukwatta E. Convolutional neural network-based approach for segmentation of left ventricle myocardial scar from 3D late gadolinium enhancement MR images. Med Phys, 2019, 46(4): 1740-1751.
36.	Fahmy AS, Neisius U, Chan RH, et al. Three-dimensional deep convolutional neural networks for automated myocardial scar quantification in hypertrophic cardiomyopathy: A multicenter multivendor study. Radiology, 2020, 294(1): 52-60.
37.	Yang G, Zhuang X, Khan H, et al. Fully automatic segmentation and objective assessment of atrial scars for long-standing persistent atrial fibrillation patients using late gadolinium-enhanced MRI. Med Phys, 2018, 45(4): 1562-1576.
38.	Tao Q, Ipek EG, Shahzad R, et al. Fully automatic segmentation of left atrium and pulmonary veins in late gadolinium-enhanced MRI: Towards objective atrial scar assessment. J Magn Reson Imaging, 2016, 44(2): 346-354.
39.	Kelm BM, Mittal S, Zheng Y, et al. Detection, grading and classification of coronary stenoses in computed tomography angiography. Med Image Comput Comput Assist Interv, 2011, 14(Pt 3): 25-32.
40.	Zreik M, van Hamersvelt RW, Wolterink JM, et al. A recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography. IEEE Trans Med Imaging, 2019, 38(7): 1588-1598.
41.	Ali I, Selen O. Cardiac arrhythmia detection using deep learning. Proc Comp Sci, 2017: 120268-275.
42.	Attia ZI, Kapa S, Lopez-Jimenez F, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med, 2019, 25(1): 70-74.
43.	Galloway CD, Valys AV, Shreibati JB, et al. Development and validation of a deep-learning model to screen for hyperkalemia from the electrocardiogram. JAMA Cardiol, 2019, 4(5): 428-436.
44.	Johnsson J, Björnsson O, Andersson P, et al. Artificial neural networks improve early outcome prediction and risk classification in out-of-hospital cardiac arrest patients admitted to intensive care. Crit Care, 2020, 24(1): 474.
45.	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. Eur Heart J, 2017, 38(7): 500-507.
46.	van Rosendael AR, Maliakal G, Kolli KK, et al. Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification; Insights from the CONFIRM registry. J Cardiovasc Comput Tomogr, 2018, 12(3): 204-209.
47.	Johnson KM, Johnson HE, Zhao Y, et al. Scoring of coronary artery disease characteristics on coronary CT angiograms by using machine learning. Radiology, 2019, 292(2): 354-362.
48.	Lee HC, Yoon HK, Nam K, et al. Derivation and validation of machine learning approaches to predict acute kidney injury after cardiac surgery. J Clin Med, 2018, 7(10): 322.
49.	Tseng PY, Chen YT, Wang CH, et al. Prediction of the development of acute kidney injury following cardiac surgery by machine learning. Crit Care, 2020, 24(1): 478.
50.	Li Y, Xu J, Wang Y, et al. A novel machine learning algorithm, Bayesian networks model, to predict the high-risk patients with cardiac surgery-associated acute kidney injury. Clin Cardiol, 2020, 43(7): 752-761.
51.	Kartal E, Balaban ME. Machine learning techniques in cardiac risk assessment. Turk Gogus Kalp Damar Cerrahisi Derg, 2018, 26(3): 394-401.
52.	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. PLoS One, 2017, 12(1): e0169772.
53.	Kwon JM, Kim KH, Jeon KH, et al. Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography. Echocardiography, 2019, 36(2): 213-218.
54.	Kilic A, Goyal A, Miller JK, et al. Predictive utility of a machine learning algorithm in estimating mortality risk in cardiac surgery. Ann Thorac Surg, 2020, 109(6): 1811-1819.
55.	Fernandes MPB, Armengol de la Hoz M, Rangasamy V, et al. Machine learning models with preoperative risk factors and intraoperative hypotension parameters predict mortality after cardiac surgery. J Cardiothorac Vasc Anesth, 2021, 35(3): 857-865.
56.	Hernandez-Suarez DF, Kim Y, Villablanca P, et al. Machine learning prediction models for in-hospital mortality after transcatheter aortic valve replacement. JACC Cardiovasc Interv, 2019, 12(14): 1328-1338.
57.	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. Eur Heart J, 2019, 40(13): 1069-1077.
58.	Diller GP, Orwat S, Vahle J, et al. Prediction of prognosis in patients with tetralogy of Fallot based on deep learning imaging analysis. Heart, 2020, 106(13): 1007-1014.
59.	Ruiz VM, Saenz L, Lopez-Magallon A, et al. Early prediction of critical events for infants with single-ventricle physiology in critical care using routinely collected data. J Thorac Cardiovasc Surg, 2019, 158(1): 234-243.
60.	Chu R, Chen W, Song G, et al. Predicting the risk of adverse events in pregnant women with congenital heart disease. J Am Heart Assoc, 2020, 9(14): e016371.
61.	Alaa AM, Bolton T, Di Angelantonio E, et al. Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423, 604 UK Biobank participants. PLoS One, 2019, 14(5): e0213653.
62.	Dimopoulos AC, Nikolaidou M, Caballero FF, et al. Machine learning methodologies versus cardiovascular risk scores, in predicting disease risk. BMC Med Res Methodol, 2018, 18(1): 179.
63.	Naushad SM, Hussain T, Indumathi B, et al. Machine learning algorithm-based risk prediction model of coronary artery disease. Mol Biol Rep, 2018, 45(5): 901-910.

1. Sacco RL, Roth GA, Reddy KS, et al. The heart of 25 by 25: Achieving the goal of reducing global and regional premature deaths from cardiovascular diseases and stroke: A modeling study from the American Heart Association and World Heart Federation. Circulation, 2016, 133(23): e674-e690.
2. Zhou M, Wang H, Zhu J, et al. Cause-specific mortality for 240 causes in China during 1990-2013: A systematic subnational analysis for the Global Burden of Disease Study 2013. Lancet, 2016, 387(10015): 251-272.
3. Goff DC, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol, 2014, 63(25 Pt B): 2935-2959.
4. Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Predicting cardiovascular risk in England and Wales: Prospective derivation and validation of QRISK2. BMJ, 2008, 336(7659): 1475-1482.
5. D'Agostino RB, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: The Framingham Heart Study. Circulation, 2008, 117(6): 743-753.
6. Ridker PM, Buring JE, Rifai N, et al. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: The Reynolds Risk Score. JAMA, 2007, 297(6): 611-619.
7. Alsharqi M, Woodward WJ, Mumith JA, et al. Artificial intelligence and echocardiography. Echo Res Pract, 2018, 5(4): R115-R125.
8. Gao Xiaohong, Li Wei, Loomes Martin, et al. A fused deep learning architecture for viewpoint classification of echocardiography. Inform Fusion, 2016, 36: 103-113.
9. Blansit K, Retson T, Masutani E, et al. Deep learning-based prescription of cardiac MRI planes. Radiol Artif Intell, 2019, 1(6): e180069.
10. Carmel H, Devos D, Lu XG. Fully automatic planning of the long-axis views of the heart. J Cardiovasc Magn Reson, 2013, 15(Suppl 1): O54.
11. Lorch B, Vaillant G, Baumgartner C, et al. Automated detection of motion artefacts in MR imaging using decision forests. J Med Eng, 2017, 2017: 4501647.
12. Hauptmann A, Arridge S, Lucka F, et al. Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning-proof of concept in congenital heart disease. Magn Reson Med, 2019, 81(2): 1143-1156.
13. Leclerc S, Grenier T, Espinosa F, et al. A fully automatic and multi-structural segmentation of the left ventricle and the myocardium on highly heterogeneous 2D echocardiographic data. Washington: 2017 IEEE International Ultrasonics Symposium (IUS).
14. Leclerc S, Smistad E, Pedrosa J, et al. Deep learning for segmentation using an open large-scale dataset in 2D echocardiography. IEEE Trans Med Imaging, 2019, 38(9): 2198-2210.
15. Suinesiaputra A, Bluemke DA, Cowan BR, et al. Quantification of LV function and mass by cardiovascular magnetic resonance: Multi-center variability and consensus contours. J Cardiovasc Magn Reson, 2015, 17(1): 63.
16. Bernard O, Lalande A, Zotti C, et al. Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved? IEEE Trans Med Imaging, 2018, 37(11): 2514-2525.
17. Bai W, Sinclair M, Tarroni G, et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. J Cardiovasc Magn Reson, 2018, 20(1): 65.
18. Oktay O, Ferrante E, Kamnitsas K, et al. Anatomically Constrained Neural Networks (ACNNs): Application to cardiac image enhancement and segmentation. IEEE Trans Med Imaging, 2018, 37(2): 384-395.
19. Zheng Y, Barbu A, Georgescu B, et al. Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans Med Imaging, 2008, 27(11): 1668-1681.
20. Gurpreet S, Subhi A, Gabriel M, et al. Deep learning based automatic segmentation of cardiac computed tomography. J Am Coll Cardiol, 2019, 73(Suppl 9): 1643.
21. Cannesson M, Tanabe M, Suffoletto MS, et al. A novel two-dimensional echocardiographic image analysis system using artificial intelligence-learned pattern recognition for rapid automated ejection fraction. J Am Coll Cardiol, 2007, 49(2): 217-226.
22. Madani A, Ong JR, Tibrewal A, et al. Deep echocardiography: Data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease. NPJ Digit Med, 2018, 1: 59.
23. Sanchez-Martinez S, Duchateau N, Erdei T, et al. Characterization of myocardial motion patterns by unsupervised multiple kernel learning. Med Image Anal, 2017, 35: 70-82.
24. Knackstedt C, Bekkers SC, Schummers G, et al. Fully automated versus standard tracking of left ventricular ejection fraction and longitudinal strain: The FAST-EFs multicenter study. J Am Coll Cardiol, 2015, 66(13): 1456-1466.
25. Lang RM, Badano LP, Mor-Avi V, et al. Recommendations for cardiac chamber quantification by echocardiography in adults: An update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J Am Soc Echocardiogr, 2015, 28(1): 1-39.
26. Narang A, Mor-Avi V, Prado A, et al. Machine learning based automated dynamic quantification of left heart chamber volumes. Eur Heart J Cardiovasc Imaging, 2019, 20(5): 541-549.
27. Tsang W, Salgo IS, Medvedofsky D, et al. Transthoracic 3D Echocardiographic left heart chamber quantification using an automated adaptive analytics algorithm. JACC Cardiovasc Imaging, 2016, 9(7): 769-782.
28. Volpato V, Mor-Avi V, Narang A, et al. Automated, machine learning-based, 3D echocardiographic quantification of left ventricular mass. Echocardiography, 2019, 36(2): 312-319.
29. Genovese D, Rashedi N, Weinert L, et al. Machine learning-based three-dimensional echocardiographic quantification of right ventricular size and function: Validation against cardiac magnetic resonance. J Am Soc Echocardiogr, 2019, 32(8): 969-977.
30. Suinesiaputra A, Sanghvi MM, Aung N, et al. Fully-automated left ventricular mass and volume MRI analysis in the UK Biobank population cohort: Evaluation of initial results. Int J Cardiovasc Imaging, 2018, 34(2): 281-291.
31. Ngo TA, Lu Z, Carneiro G. Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance. Med Image Anal, 2017, 35: 159-171.
32. Marino M, Veronesi F, Corsi C. Fully automated assessment of left ventricular volumes and mass from cardiac magnetic resonance images. Annu Int Conf IEEE Eng Med Biol Soc, 2014, 2014: 1079-1082.
33. Avendi MR, Kheradvar A, Jafarkhani H. Automatic segmentation of the right ventricle from cardiac MRI using a learning-based approach. Magn Reson Med, 2017, 78(6): 2439-2448.
34. Petitjean C, Zuluaga MA, Bai W, et al. Right ventricle segmentation from cardiac MRI: A collation study. Med Image Anal, 2015, 19(1): 187-202.
35. Zabihollahy F, White JA, Ukwatta E. Convolutional neural network-based approach for segmentation of left ventricle myocardial scar from 3D late gadolinium enhancement MR images. Med Phys, 2019, 46(4): 1740-1751.
36. Fahmy AS, Neisius U, Chan RH, et al. Three-dimensional deep convolutional neural networks for automated myocardial scar quantification in hypertrophic cardiomyopathy: A multicenter multivendor study. Radiology, 2020, 294(1): 52-60.
37. Yang G, Zhuang X, Khan H, et al. Fully automatic segmentation and objective assessment of atrial scars for long-standing persistent atrial fibrillation patients using late gadolinium-enhanced MRI. Med Phys, 2018, 45(4): 1562-1576.
38. Tao Q, Ipek EG, Shahzad R, et al. Fully automatic segmentation of left atrium and pulmonary veins in late gadolinium-enhanced MRI: Towards objective atrial scar assessment. J Magn Reson Imaging, 2016, 44(2): 346-354.
39. Kelm BM, Mittal S, Zheng Y, et al. Detection, grading and classification of coronary stenoses in computed tomography angiography. Med Image Comput Comput Assist Interv, 2011, 14(Pt 3): 25-32.
40. Zreik M, van Hamersvelt RW, Wolterink JM, et al. A recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography. IEEE Trans Med Imaging, 2019, 38(7): 1588-1598.
41. Ali I, Selen O. Cardiac arrhythmia detection using deep learning. Proc Comp Sci, 2017: 120268-275.
42. Attia ZI, Kapa S, Lopez-Jimenez F, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med, 2019, 25(1): 70-74.
43. Galloway CD, Valys AV, Shreibati JB, et al. Development and validation of a deep-learning model to screen for hyperkalemia from the electrocardiogram. JAMA Cardiol, 2019, 4(5): 428-436.
44. Johnsson J, Björnsson O, Andersson P, et al. Artificial neural networks improve early outcome prediction and risk classification in out-of-hospital cardiac arrest patients admitted to intensive care. Crit Care, 2020, 24(1): 474.
45. 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. Eur Heart J, 2017, 38(7): 500-507.
46. van Rosendael AR, Maliakal G, Kolli KK, et al. Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification; Insights from the CONFIRM registry. J Cardiovasc Comput Tomogr, 2018, 12(3): 204-209.
47. Johnson KM, Johnson HE, Zhao Y, et al. Scoring of coronary artery disease characteristics on coronary CT angiograms by using machine learning. Radiology, 2019, 292(2): 354-362.
48. Lee HC, Yoon HK, Nam K, et al. Derivation and validation of machine learning approaches to predict acute kidney injury after cardiac surgery. J Clin Med, 2018, 7(10): 322.
49. Tseng PY, Chen YT, Wang CH, et al. Prediction of the development of acute kidney injury following cardiac surgery by machine learning. Crit Care, 2020, 24(1): 478.
50. Li Y, Xu J, Wang Y, et al. A novel machine learning algorithm, Bayesian networks model, to predict the high-risk patients with cardiac surgery-associated acute kidney injury. Clin Cardiol, 2020, 43(7): 752-761.
51. Kartal E, Balaban ME. Machine learning techniques in cardiac risk assessment. Turk Gogus Kalp Damar Cerrahisi Derg, 2018, 26(3): 394-401.
52. 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. PLoS One, 2017, 12(1): e0169772.
53. Kwon JM, Kim KH, Jeon KH, et al. Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography. Echocardiography, 2019, 36(2): 213-218.
54. Kilic A, Goyal A, Miller JK, et al. Predictive utility of a machine learning algorithm in estimating mortality risk in cardiac surgery. Ann Thorac Surg, 2020, 109(6): 1811-1819.
55. Fernandes MPB, Armengol de la Hoz M, Rangasamy V, et al. Machine learning models with preoperative risk factors and intraoperative hypotension parameters predict mortality after cardiac surgery. J Cardiothorac Vasc Anesth, 2021, 35(3): 857-865.
56. Hernandez-Suarez DF, Kim Y, Villablanca P, et al. Machine learning prediction models for in-hospital mortality after transcatheter aortic valve replacement. JACC Cardiovasc Interv, 2019, 12(14): 1328-1338.
57. 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. Eur Heart J, 2019, 40(13): 1069-1077.
58. Diller GP, Orwat S, Vahle J, et al. Prediction of prognosis in patients with tetralogy of Fallot based on deep learning imaging analysis. Heart, 2020, 106(13): 1007-1014.
59. Ruiz VM, Saenz L, Lopez-Magallon A, et al. Early prediction of critical events for infants with single-ventricle physiology in critical care using routinely collected data. J Thorac Cardiovasc Surg, 2019, 158(1): 234-243.
60. Chu R, Chen W, Song G, et al. Predicting the risk of adverse events in pregnant women with congenital heart disease. J Am Heart Assoc, 2020, 9(14): e016371.
61. Alaa AM, Bolton T, Di Angelantonio E, et al. Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423, 604 UK Biobank participants. PLoS One, 2019, 14(5): e0213653.
62. Dimopoulos AC, Nikolaidou M, Caballero FF, et al. Machine learning methodologies versus cardiovascular risk scores, in predicting disease risk. BMC Med Res Methodol, 2018, 18(1): 179.
63. Naushad SM, Hussain T, Indumathi B, et al. Machine learning algorithm-based risk prediction model of coronary artery disease. Mol Biol Rep, 2018, 45(5): 901-910.

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