REFERENCES
1. Bergami M, Scarpone M, Bugiardini R, Cenko E, Manfrini O. Sex beyond cardiovascular risk factors and clinical biomarkers of cardiovascular disease. Rev Cardiovasc Med. 2022;23:19.
2. Zhou SS, Jin JP, Wang JQ, et al. miRNAS in cardiovascular diseases: potential biomarkers, therapeutic targets and challenges. Acta Pharmacol Sin. 2018;39:1073-84.
3. Westerlund AM, Hawe JS, Heinig M, Schunkert H. Risk prediction of cardiovascular events by exploration of molecular data with explainable artificial intelligence. Int J Mol Sci. 2021;22:10291.
4. Bivard A, Churilov L, Parsons M. Artificial intelligence for decision support in acute stroke - current roles and potential. Nat Rev Neurol. 2020;16:575-85.
5. Reel PS, Reel S, van Kralingen JC, et al. Machine learning for classification of hypertension subtypes using multi-omics: a multi-centre, retrospective, data-driven study. EBioMedicine. 2022;84:104276.
6. Poss AM, Maschek JA, Cox JE, et al. Machine learning reveals serum sphingolipids as cholesterol-independent biomarkers of coronary artery disease. J Clin Invest. 2020;130:1363-76.
7. de Gonzalo-Calvo D, Martínez-Camblor P, Bär C, et al. Improved cardiovascular risk prediction in patients with end-stage renal disease on hemodialysis using machine learning modeling and circulating microribonucleic acids. Theranostics. 2020;10:8665-76.
8. Schlant RC, Adolph RJ, DiMarco JP, et al. Guidelines for electrocardiography. A report of the American college of cardiology/American heart association task force on assessment of diagnostic and therapeutic cardiovascular procedures (committee on electrocardiography). Circulation. 1992;85:1221-8.
9. Hannun AY, Rajpurkar P, Haghpanahi M, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med. 2019;25:65-9.
10. Shah AP, Rubin SA. Errors in the computerized electrocardiogram interpretation of cardiac rhythm. J Electrocardiol. 2007;40:385-90.
11. Guglin ME, Thatai D. Common errors in computer electrocardiogram interpretation. Int J Cardiol. 2006;106:232-7.
12. Poon K, Okin PM, Kligfield P. Diagnostic performance of a computer-based ECG rhythm algorithm. J Electrocardiol. 2005;38:235-8.
14. Smith SW, Rapin J, Li J, et al. A deep neural network for 12-lead electrocardiogram interpretation outperforms a conventional algorithm, and its physician overread, in the diagnosis of atrial fibrillation. Int J Cardiol Heart Vasc. 2019;25:100423.
15. Liu CW, Wu FH, Hu YL, et al. Left ventricular hypertrophy detection using electrocardiographic signal. Sci Rep. 2023;13:2556.
16. Attia ZI, Kapa S, Lopez-Jimenez F, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019;25:70-4.
17. 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. Lancet. 2019;394:861-7.
18. Raghunath S, Ulloa Cerna AE, Jing L, et al. Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network. Nat Med. 2020;26:886-91.
19. Poungponsri S, Yu XH. An adaptive filtering approach for electrocardiogram (ECG) signal noise reduction using neural networks. Neurocomputing. 2013;117:206-13.
20. Jadhav SM, Nalbalwar SL, Ghatol A. Artificial neural network based cardiac arrhythmia classification using ECG signal data. In: 2010 international conference on electronics and information engineering; 1-3 Aug 2010; Kyoto, Japan.
21. Acharya UR, Oh SL, Hagiwara Y, et al. A deep convolutional neural network model to classify heartbeats. Comput Biol Med. 2017;89:389-96.
22. Haseena HH, Mathew AT, Paul JK. Fuzzy clustered probabilistic and multi layered feed forward neural networks for electrocardiogram arrhythmia classification. J Med Syst. 2011;35:179-88.
23. Sayantan G, Kien PT, Kadambari KV. Classification of ECG beats using deep belief network and active learning. Med Biol Eng Comput. 2018;56:1887-98.
24. Tateno K, Glass L. Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and deltaRR intervals. Med Biol Eng Comput. 2001;39:664-71.
25. Dash S, Chon KH, Lu S, Raeder EA. Automatic real time detection of atrial fibrillation. Ann Biomed Eng. 2009;37:1701-9.
26. Huang C, Ye S, Chen H, Li D, He F, Tu Y. A novel method for detection of the transition between atrial fibrillation and sinus rhythm. IEEE Trans Biomed Eng. 2011;58:1113-9.
27. Babaeizadeh S, Gregg RE, Helfenbein ED, Lindauer JM, Zhou SH. Improvements in atrial fibrillation detection for real-time monitoring. J Electrocardiol. 2009;42:522-6.
28. Jiang K, Huang C, Ye SM, Chen H. High accuracy in automatic detection of atrial fibrillation for Holter monitoring. J Zhejiang Univ Sci B. 2012;13:751-6.
29. Xia Y, Wulan N, Wang K, Zhang H. Detecting atrial fibrillation by deep convolutional neural networks. Comput Biol Med. 2018;93:84-92.
30. Acharya UR, Fujita H, Oh SL, et al. Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network. Future Gener Comput Syst. 2018;79:952-9.
31. Sabut S, Pandey O, Mishra BSP, Mohanty M. Detection of ventricular arrhythmia using hybrid time-frequency-based features and deep neural network. Phys Eng Sci Med. 2021;44:135-45.
32. Chang KC, Hsieh PH, Wu MY, et al. Usefulness of multi-labelling artificial intelligence in detecting rhythm disorders and acute ST-elevation myocardial infarction on 12-lead electrocardiogram. Eur Heart J Digit Health. 2021;2:299-310.
33. Zheng J, Fu G, Struppa D, et al. A high precision machine learning-enabled system for predicting idiopathic ventricular arrhythmia origins. Front Cardiovasc Med. 2022;9:809027.
34. Raghunath S, Pfeifer JM, Ulloa-Cerna AE, et al. Deep neural networks can predict new-onset atrial fibrillation from the 12-lead ECG and help identify those at risk of atrial fibrillation-related stroke. Circulation. 2021;143:1287-98.
35. Hsu CY, Liu PY, Liu SH, Kwon Y, Lavie CJ, Lin GM. Machine learning for electrocardiographic features to identify left atrial enlargement in young adults: CHIEF heart study. Front Cardiovasc Med. 2022;9:840585.
36. Zhao X, Huang G, Wu L, et al. Deep learning assessment of left ventricular hypertrophy based on electrocardiogram. Front Cardiovasc Med. 2022;9:952089.
37. Grogan M, Lopez-Jimenez F, Cohen-Shelly M, et al. Artificial intelligence-enhanced electrocardiogram for the early detection of cardiac amyloidosis. Mayo Clin Proc. 2021;96:2768-78.
38. Kwon JM, Kim KH, Jeon KH, et al. Development and validation of deep-learning algorithm for electrocardiography-based heart failure identification. Korean Circ J. 2019;49:629-39.
39. McDonagh TA, McDonald K, Maisel AS. Screening for asymptomatic left ventricular dysfunction using B-type natriuretic peptide. Congest Heart Fail. 2008;14:5-8.
40. Dey M, Omar N, Ullah MA. Temporal feature-based classification into myocardial infarction and other CVDs merging CNN and Bi-LSTM from ECG signal. IEEE Sensors J. 2021;21:21688-95.
41. Jahmunah V, Ng EYK, San TR, Acharya UR. Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals. Comput Biol Med. 2021;134:104457.
42. Yadav SS, More SB, Jadhav SM, Sutar SR. Convolutional neural networks based diagnosis of myocardial infarction in electrocardiograms. In: 2021 international conference on computing, communication, and intelligent systems (ICCCIS); 19-20 Feb 2021; Greater Noida, India. pp. 581-6.
43. Hasbullah S, Mohd Zahid MS, Mandala S. Detection of myocardial infarction using hybrid models of convolutional neural network and recurrent neural network. BioMedInformatics. 2023;3:478-92.
44. Xiong P, Xue Y, Zhang J, et al. Localization of myocardial infarction with multi-lead ECG based on DenseNet. Comput Meth Prog Bio. 2021;203:106024.
45. Ibrahim L, Mesinovic M, Yang KW, Eid MA. Explainable prediction of acute myocardial infarction using machine learning and shapley values. IEEE Access. 2020;8:210410-7.
46. Gustafsson S, Gedon D, Lampa E, et al. Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients. Sci Rep. 2022;12:19615.
47. Fiorina L, Maupain C, Gardella C, et al. Evaluation of an ambulatory ECG analysis platform using deep neural networks in routine clinical practice. J Am Heart Assoc. 2022;11:e026196.
48. Guo SL, Han LN, Liu HW, Si QJ, Kong DF, Guo FS. The future of remote ECG monitoring systems. J Geriatr Cardiol. 2016;13:528-30.
49. Abdou A, Krishnan S. Horizons in single-lead ECG analysis from devices to data. Front Signal Process. 2022;2:866047.
50. Mannhart D, Lischer M, Knecht S, et al. Clinical validation of 5 direct-to-consumer wearable smart devices to detect atrial fibrillation: BASEL wearable study. JACC Clin Electrocardiol. 2023;9:232-42.
51. Kim M, Kang Y, You SC, et al. Artificial intelligence predicts clinically relevant atrial high-rate episodes in patients with cardiac implantable electronic devices. Sci Rep. 2022;12:37.
52. Shakibfar S, Krause O, Lund-Andersen C, et al. Predicting electrical storms by remote monitoring of implantable cardioverter-defibrillator patients using machine learning. Europace. 2019;21:268-74.
53. Cheng Y, Ousdigian KT, Sarkar S, Koehler J, Cho YK, Kloosterman EM. B-PO04-036 innovative artificial intelligence application reduces false pause alerts while maintaining perfect true pause alert sensitivity for insertable cardiac monitors. Heart Rhythm. 2021;18:S293-4.
54. Radtke AP, Ousdigian KT, Haddad TD, Koehler JL, Colombowala IK. B-AB24-04 artificial intelligence enables dramatic reduction of false atrial fibrillation alerts from insertable cardiac monitors. Heart Rhythm. 2021;18:S47.
55. McSharry PE, Clifford GD, Tarassenko L, Smith LA. A dynamical model for generating synthetic electrocardiogram signals. IEEE Trans Biomed Eng. 2003;50:289-94.
56. Li Z, Ma M. ECG modeling with DFG. In: 2005 IEEE engineering in medicine and biology 27th annual conference 2005; 17-18 Jan 2006; Shanghai, China.
57. Sameni R, Clifford GD, Jutten C, Shamsollahi MB. Multichannel ECG and noise modeling: application to maternal and fetal ECG signals. EURASIP J Adv Signal Process. 2007;2007:043407.
58. Clifford GD, Nemati S, Sameni R. An artificial vector model for generating abnormal electrocardiographic rhythms. Physiol Meas. 2010;31:595-609.
59. Roonizi EK, Sameni R. Morphological modeling of cardiac signals based on signal decomposition. Comput Biol Med. 2013;43:1453-61.
60. Wulan N, Wang W, Sun P, Wang K, Xia Y, Zhang H. Generating electrocardiogram signals by deep learning. Neurocomputing. 2020;404:122-36.
61. Smith-Bindman R, Miglioretti DL, Larson EB. Rising use of diagnostic medical imaging in a large integrated health system. Health Aff. 2008;27:1491-502.
62. Lim LJ, Tison GH, Delling FN. Artificial intelligence in cardiovascular imaging. Methodist Debakey Cardiovasc J. 2020;16:138-45.
63. Kusunose K, Abe T, Haga A, et al. A deep learning approach for assessment of regional wall motion abnormality from echocardiographic images. JACC Cardiovasc Imag. 2020;13:374-81.
64. Madani A, Arnaout R, Mofrad M, Arnaout R. Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit Med. 2018;1:6.
65. Zhang J, Gajjala S, Agrawal P, et al. Fully automated echocardiogram interpretation in clinical practice. Circulation. 2018;138:1623-35.
66. Tabassian M, Alessandrini M, Herbots L, et al. Machine learning of the spatio-temporal characteristics of echocardiographic deformation curves for infarct classification. Int J Cardiovasc Imag. 2017;33:1159-67.
67. Sengupta PP, Huang YM, Bansal M, et al. Cognitive machine-learning algorithm for cardiac imaging: a pilot study for differentiating constrictive pericarditis from restrictive cardiomyopathy. Circ Cardiovasc Imag. 2016;9:e004330.
68. Heitzinger G, Spinka G, Koschatko S, et al. A streamlined, machine learning-derived approach to risk-stratification in heart failure patients with secondary tricuspid regurgitation. Eur Heart J Cardiovasc Imag. 2023;24:588-97.
69. Lei J, Wang YG, Bhatta L, et al. Ventricular geometry-regularized QRSd predicts cardiac resynchronization therapy response: machine learning from crosstalk between electrocardiography and echocardiography. Int J Cardiovasc Imag. 2019;35:1221-9.
70. Fotaki A, Puyol-Antón E, Chiribiri A, Botnar R, Pushparajah K, Prieto C. Artificial intelligence in cardiac MRI: is clinical adoption forthcoming? Front Cardiovasc Med. 2021;8:818765.
71. Chen C, Bai W, Davies RH, et al. Improving the generalizability of convolutional neural network-based segmentation on CMR images. Front Cardiovasc Med. 2020;7:105.
72. He Y, Qin W, Wu Y, et al. Automatic left ventricle segmentation from cardiac magnetic resonance images using a capsule network. J Xray Sci Technol. 2020;28:541-53.
73. Bhuva AN, Bai W, Lau C, et al. A multicenter, scan-rescan, human and machine learning CMR study to test generalizability and precision in imaging biomarker analysis. Circ Cardiovasc Imag. 2019;12:e009214.
74. Augusto JB, Davies RH, Bhuva AN, et al. Diagnosis and risk stratification in hypertrophic cardiomyopathy using machine learning wall thickness measurement: a comparison with human test-retest performance. Lancet Digit Health. 2021;3:e20-8.
75. Eichhorn C, Greulich S, Bucciarelli-Ducci C, Sznitman R, Kwong RY, Gräni C. Multiparametric cardiovascular magnetic resonance approach in diagnosing, monitoring, and prognostication of myocarditis. JACC Cardiovasc Imag. 2022;15:1325-38.
76. Fahmy AS, Rausch J, Neisius U, et al. Automated cardiac MR scar quantification in hypertrophic cardiomyopathy using deep convolutional neural networks. JACC Cardiovasc Imag. 2018;11:1917-8.
77. Fahmy AS, El-Rewaidy H, Nezafat M, Nakamori S, Nezafat R. Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks. J Cardiovasc Magn Reson. 2019;21:7.
78. Neisius U, El-Rewaidy H, Nakamori S, Rodriguez J, Manning WJ, Nezafat R. Radiomic analysis of myocardial native T1 imaging discriminates between hypertensive heart disease and hypertrophic cardiomyopathy. JACC Cardiovasc Imag. 2019;12:1946-54.
79. Wang J, Yang F, Liu W, et al. Radiomic analysis of native T1 mapping images discriminates between MYH7 and MYBPC3-related hypertrophic cardiomyopathy. J Magn Reson Imag. 2020;52:1714-21.
80. Xue H, Artico J, Davies RH, et al. Automated in-line artificial intelligence measured global longitudinal shortening and mitral annular plane systolic excursion: reproducibility and prognostic significance. J Am Heart Assoc. 2022;11:e023849.
81. Coenen A, Kim YH, Kruk M, et al. Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography-based fractional flow reserve: result from the MACHINE consortium. Circ Cardiovasc Imag. 2018;11:e007217.
82. Kang D, Dey D, Slomka PJ, et al. Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography. J Med Imag. 2015;2:014003.
83. Liao S, Mo Z, Zeng M, et al. Fast and low-dose medical imaging generation empowered by hybrid deep-learning and iterative reconstruction. Cell Rep Med. 2023;4:101119.
84. 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;38:500-7.
85. 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;12:204-9.
86. Li T, Zhang M, Qi W, Asma E, Qi J. Motion correction of respiratory-gated PET images using deep learning based image registration framework. Phys Med Biol. 2020;65:155003.
87. Nakajima K, Kudo T, Nakata T, et al. Diagnostic accuracy of an artificial neural network compared with statistical quantitation of myocardial perfusion images: a Japanese multicenter study. Eur J Nucl Med Mol Imag. 2017;44:2280-9.
88. Arsanjani R, Xu Y, Dey D, et al. Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population. J Nucl Cardiol. 2013;20:553-62.
89. Betancur J, Commandeur F, Motlagh M, et al. Deep learning for prediction of obstructive disease from fast myocardial perfusion SPECT: a multicenter study. JACC Cardiovasc Imag. 2018;11:1654-63.
90. Betancur J, Otaki Y, Motwani M, et al. Prognostic value of combined clinical and myocardial perfusion imaging data using machine learning. JACC Cardiovasc Imag. 2018;11:1000-9.
91. Arsanjani R, Dey D, Khachatryan T, et al. Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population. J Nucl Cardiol. 2015;22:877-84.
92. Virani SS, Alonso A, Benjamin EJ, et al. Heart disease and stroke statistics-2020 update: a report from the american heart association. Circulation. 2020;141:e139-596.
93. Maragna R, Giacari CM, Guglielmo M, et al. Artificial intelligence based multimodality imaging: a new frontier in coronary artery disease management. Front Cardiovasc Med. 2021;8:736223.
94. Acharya UR, Fujita H, Adam M, et al. Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: a comparative study. Inf Sci. 2017;377:17-29.
95. Han C, Shi L. ML-ResNet: a novel network to detect and locate myocardial infarction using 12 leads ECG. Comput Meth Prog Bio. 2020;185:105138.
96. Lih OS, Jahmunah V, San TR, et al. Comprehensive electrocardiographic diagnosis based on deep learning. Artif Intell Med. 2020;103:101789.
97. McCarthy CP, Neumann JT, Michelhaugh SA, et al. Derivation and external validation of a high-sensitivity cardiac troponin-based proteomic model to predict the presence of obstructive coronary artery disease. J Am Heart Assoc. 2020;9:e017221.
98. Sabatino J, De Rosa S, Leo I, et al. Non-invasive myocardial work is reduced during transient acute coronary occlusion. PLoS One. 2020;15:e0244397.
99. Sabatino J, De Rosa S, Leo I, et al. Prediction of significant coronary artery disease through advanced echocardiography: role of non-invasive myocardial work. Front Cardiovasc Med. 2021;8:719603.
100. Raghavendra U, En Wei JK, Gudigar A, et al. Automated diagnosis and assessment of cardiac structural alteration in hypertension ultrasound images. Contrast Media Mol Imag. 2022;2022:5616939.
101. Vidya KS, Ng EYK, Acharya UR, Chou SM, Tan RS, Ghista DN. Computer-aided diagnosis of myocardial infarction using ultrasound images with DWT, GLCM and HOS methods: a comparative study. Comput Biol Med. 2015;62:86-93.
102. Glessgen CG, Boulougouri M, Vallée JP, et al. Artificial intelligence-based opportunistic detection of coronary artery stenosis on aortic computed tomography angiography in emergency department patients with acute chest pain. Eur Heart J Open. 2023;3:oead088.
103. Knott KD, Seraphim A, Augusto JB, et al. The prognostic significance of quantitative myocardial perfusion: an artificial intelligence-based approach using perfusion mapping. Circulation. 2020;141:1282-91.
104. Farooq V, van Klaveren D, Steyerberg EW, et al. Anatomical and clinical characteristics to guide decision making between coronary artery bypass surgery and percutaneous coronary intervention for individual patients: development and validation of SYNTAX score II. Lancet. 2013;381:639-50.
105. De Rosa S, Polimeni A, De Velli G, et al. Reliability of instantaneous wave-free ratio (iFR) for the evaluation of left main coronary artery lesions. J Clin Med. 2019;8:1143.
106. De Rosa S, Polimeni A, Petraco R, Davies JE, Indolfi C. Diagnostic performance of the instantaneous wave-free ratio: comparison with fractional flow reserve. Circ Cardiovasc Interv. 2018;11:e004613.
107. SCOT-HEART Investigators. Coronary CT angiography and 5-year risk of myocardial infarction. N Engl J Med. 2018;379:924-33.
108. Douglas PS, De Bruyne B, Pontone G, et al. 1-year outcomes of FFRCT-guided care in patients with suspected coronary disease: the PLATFORM study. J Am Coll Cardiol. 2016;68:435-45.
109. Tesche C, Gray HN. Machine learning and deep neural networks applications in coronary flow assessment: the case of computed tomography fractional flow reserve. J Thorac Imag. 2020;35:S66-71.
110. Celeng C, Leiner T, Maurovich-Horvat P, et al. Anatomical and functional computed tomography for diagnosing hemodynamically significant coronary artery disease: a meta-analysis. JACC Cardiovasc Imag. 2019;12:1316-25.
111. Qiao HY, Tang CX, Schoepf UJ, et al. Impact of machine learning-based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease. Eur Radiol. 2020;30:5841-51.
112. Curzen N, Nicholas Z, Stuart B, et al. Fractional flow reserve derived from computed tomography coronary angiography in the assessment and management of stable chest pain: the FORECAST randomized trial. Eur Heart J. 2021;42:3844-52.
113. Yang J, Shan D, Wang X, et al. On-site computed tomography-derived fractional flow reserve to guide management of patients with stable coronary artery disease: the TARGET randomized trial. Circulation. 2023;147:1369-81.
114. Kumamaru KK, Fujimoto S, Otsuka Y, et al. Diagnostic accuracy of 3D deep-learning-based fully automated estimation of patient-level minimum fractional flow reserve from coronary computed tomography angiography. Eur Heart J Cardiovasc Imag. 2020;21:437-45.
115. Min HS, Yoo JH, Kang SJ, et al. Detection of optical coherence tomography-defined thin-cap fibroatheroma in the coronary artery using deep learning. EuroIntervention. 2020;16:404-12.
116. Cho H, Kang SJ, Min HS, et al. Intravascular ultrasound-based deep learning for plaque characterization in coronary artery disease. Atherosclerosis. 2021;324:69-75.
117. Cook CM, Warisawa T, Howard JP, et al. Algorithmic versus expert human interpretation of instantaneous wave-free ratio coronary pressure-wire pull back data. JACC Cardiovasc Interv. 2019;12:1315-24.
118. Miyoshi T, Higaki A, Kawakami H, Yamaguchi O. Automated interpretation of the coronary angioscopy with deep convolutional neural networks. Open Heart. 2020;7:e001177.
119. Serruys PW, Chichareon P, Modolo R, et al. The SYNTAX score on its way out or … towards artificial intelligence: part I. EuroIntervention. 2020;16:44-59.
120. Serruys PW, Chichareon P, Modolo R, et al. The SYNTAX score on its way out or … towards artificial intelligence: part II. EuroIntervention. 2020;16:60-75.
121. Conrad N, Judge A, Tran J, et al. Temporal trends and patterns in heart failure incidence: a population-based study of 4 million individuals. Lancet. 2018;391:572-80.
122. 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the global burden of disease study 2017. Lancet. 2018;392:1789-858.
125. Smeets M, Vaes B, Mamouris P, et al. Burden of heart failure in Flemish general practices: a registry-based study in the Intego database. BMJ Open. 2019;9:e022972.
126. Gerber Y, Weston SA, Redfield MM, et al. A contemporary appraisal of the heart failure epidemic in Olmsted County, Minnesota, 2000 to 2010. JAMA Intern Med. 2015;175:996-1004.
127. Boccuto F, De Rosa S, Torella D, Veltri P, Guzzi PH. Will artificial intelligence provide answers to current gaps and needs in chronic heart failure? Appl Sci. 2023;13:7663.
128. Lindholm D, Fukaya E, Leeper NJ, Ingelsson E. Bioimpedance and new-onset heart failure: a longitudinal study of >500,000 individuals from the general population. J Am Heart Assoc. 2018;7:e008970.
129. Yang G, Ren Y, Pan Q, et al. A heart failure diagnosis model based on support vector machine. In 2010 3rd international conference on biomedical engineering and informatics; 16-18 Oct 2010; Yantai, China.
130. Gharehchopogh SF, Khalifehlou ZA. Neural network application in diagnosis of patient: a case study. In International Conference on Computer Networks and Information Technology; 11-13 Jul 2011; Abbottabad, Pakistan.
131. Aljaaf AJ, Al-Jumeily D, Hussain AJ, Dawson T, Fergus P, Al-Jumaily M. Predicting the likelihood of heart failure with a multi level risk assessment using decision tree. In: 2015 third international conference on technological advances in electrical, electronics and computer engineering (TAEECE); 29 April 2015-01 May 2015; Beirut, Lebanon.
132. Zheng Y, Guo X, Qin J, Xiao S. Computer-assisted diagnosis for chronic heart failure by the analysis of their cardiac reserve and heart sound characteristics. Comput Meth Prog Bio. 2015;122:372-83.
133. Gharehbaghi A, Lindén M, Babic A. An artificial intelligent-based model for detecting systolic pathological patterns of phonocardiogram based on time-growing neural network. Appl Soft Comput. 2019;83:105615.
134. Segar MW, Vaduganathan M, Patel KV, et al. Machine learning to predict the risk of incident heart failure hospitalization among patients with diabetes: the WATCH-DM risk score. Diabetes Care. 2019;42:2298-306.
135. Segar MW, Jaeger BC, Patel KV, et al. Development and validation of machine learning-based race-specific models to predict 10-year risk of heart failure: a multicohort analysis. Circulation. 2021;143:2370-83.
136. Morrill J, Qirko K, Kelly J, et al. A machine learning methodology for identification and triage of heart failure exacerbations. J Cardiovasc Transl Res. 2022;15:103-15.
137. Shah SJ, Katz DH, Selvaraj S, et al. Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation. 2015;131:269-79.
138. Kao DP, Lewsey JD, Anand IS, et al. Characterization of subgroups of heart failure patients with preserved ejection fraction with possible implications for prognosis and treatment response. Eur J Heart Fail. 2015;17:925-35.
139. Gu J, Pan JA, Lin H, Zhang JF, Wang CQ. Characteristics, prognosis and treatment response in distinct phenogroups of heart failure with preserved ejection fraction. Int J Cardiol. 2021;323:148-54.
140. Segar MW, Patel KV, Ayers C, et al. Phenomapping of patients with heart failure with preserved ejection fraction using machine learning-based unsupervised cluster analysis. Eur J Heart Fail. 2020;22:148-58.
141. Hedman ÅK, Hage C, Sharma A, et al. Identification of novel pheno-groups in heart failure with preserved ejection fraction using machine learning. Heart. 2020;106:342-9.
142. Schrub F, Oger E, Bidaut A, et al. Heart failure with preserved ejection fraction: a clustering approach to a heterogenous syndrome. Arch Cardiovasc Dis. 2020;113:381-90.
143. Gevaert AB, Tibebu S, Mamas MA, et al. Clinical phenogroups are more effective than left ventricular ejection fraction categories in stratifying heart failure outcomes. ESC Heart Fail. 2021;8:2741-54.
144. Kwon JM, Kim KH, Jeon KH, et al. Artificial intelligence algorithm for predicting mortality of patients with acute heart failure. PLoS One. 2019;14:e0219302.
145. Peng S, Huang J, Liu X, et al. Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: a retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases. Front Cardiovasc Med. 2022;9:994359.
146. Li J, Liu S, Hu Y, Zhu L, Mao Y, Liu J. Predicting mortality in intensive care unit patients with heart failure using an interpretable machine learning model: retrospective cohort study. J Med Internet Res. 2022;24:e38082.
147. Yang B, Zhu Y, Lu X, Shen C. A Novel composite indicator of predicting mortality risk for heart failure patients with diabetes admitted to intensive care unit based on machine learning. Front Endocrinol. 2022;13:917838.
148. Karwath A, Bunting KV, Gill SK, et al. Redefining β-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis. Lancet. 2021;398:1427-35.
149. Cikes M, Sanchez-Martinez S, Claggett B, et al. Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy. Eur J Heart Fail. 2019;21:74-85.
150. Zheng Y, Lu X, Georgescu B, et al. Robust object detection using marginal space learning and ranking-based multi-detector aggregation: application to left ventricle detection in 2D MRI images. In: 2009 IEEE conference on computer vision and pattern recognition; 20-25 Jun 2009; Miami, FL, USA.
151. Lu X, Jolly MP, Georgescu B, et al. Automatic view planning for cardiac MRI acquisition. In: Fichtinger G, Martel A, Peters T, editors. Medical image computing and computer-assisted intervention - MICCAI 2011. Berlin, Heidelberg: Springer; 2011. pp. 479-86.
152. Avendi MR, Kheradvar A, Jafarkhani H. A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med Image Anal. 2016;30:108-19.
153. Orwat S, Arvanitaki A, Diller GP. A new approach to modelling in adult congenital heart disease: artificial intelligence. Rev Esp Cardiol. 2021;74:573-5.
154. Hyun CM, Kim HP, Lee SM, et al. Deep learning for undersampled MRI reconstruction. Phys Med Biol. 2018;63:135007.
155. Pace DF, Dalca AV, Geva T, Powell AJ, Moghari MH, Golland P. Interactive whole-heart segmentation in congenital heart disease. In: Navab N, Hornegger J, Wells WM, Frangi AF, editors. Medical Image computing and computer-assisted intervention - MICCAI 2015. Cham: Springer International Publishing; 2015. pp. 80-8.
156. Arafati A, Hu P, Finn JP, et al. Artificial intelligence in pediatric and adult congenital cardiac MRI: an unmet clinical need. Cardiovasc Diagn Ther. 2019;9:S310-25.
157. Diller GP, Vahle J, Radke R, et al. Utility of deep learning networks for the generation of artificial cardiac magnetic resonance images in congenital heart disease. BMC Med Imag. 2020;20:113.
158. Pace DF, Dalca AV, Brosch T, et al. Iterative segmentation from limited training data: applications to congenital heart disease. In: Stoyanov D, Taylor Z, Carneiro G, et al. editors. Deep learning in medical image analysis and multimodal learning for clinical decision support. Cham: Springer International Publishing; 2018. pp. 334-42.
159. Wesołowski S, Lemmon G, Hernandez EJ, et al. An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records. PLOS Digit Health. 2022;1:e0000004.
160. 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 10019 patients. Eur Heart J. 2019;40:1069-77.
161. 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;106:1007-14.
162. Davies R, Babu-Narayan SV. Deep learning in congenital heart disease imaging: hope but not haste. Heart. 2020;106:960-1.
163. Diller GP, Lammers AE, Babu-Narayan S, et al. Denoising and artefact removal for transthoracic echocardiographic imaging in congenital heart disease: utility of diagnosis specific deep learning algorithms. Int J Cardiovasc Imag. 2019;35:2189-96.
164. Chessa M, Van De Bruaene A, Farooqi K, et al. Three-dimensional printing, holograms, computational modelling, and artificial intelligence for adult congenital heart disease care: an exciting future. Eur Heart J. 2022;43:2672-84.
165. Lu XH, Liu A, Fuh SC, et al. Recurrent disease progression networks for modelling risk trajectory of heart failure. PLoS One. 2021;16:e0245177.
166. Ishikita A, McIntosh C, Hanneman K, et al. Machine learning for prediction of adverse cardiovascular events in adults with repaired tetralogy of fallot using clinical and cardiovascular magnetic resonance imaging variables. Circ Cardiovasc Imag. 2023;16:e015205.
167. Chang Junior J, Binuesa F, Caneo LF, et al. Improving preoperative risk-of-death prediction in surgery congenital heart defects using artificial intelligence model: a pilot study. PLoS One. 2020;15:e0238199.
168. Bogani R, Theodorou A, Arnaboldi L, Wortham RH. Garbage in, toxic data out: a proposal for ethical artificial intelligence sustainability impact statements. AI Ethics. 2022:1-8.
169. Liyanage H, Liaw ST, Jonnagaddala J, et al. Artificial intelligence in primary health care: perceptions, issues, and challenges. Yearb Med Inform. 2019;28:41-6.
170. Kim DW, Jang HY, Kim KW, Shin Y, Park SH. Design characteristics of studies reporting the performance of artificial intelligence algorithms for diagnostic analysis of medical images: results from recently published papers. Korean J Radiol. 2019;20:405-10.
171. Tsopra R, Fernandez X, Luchinat C, et al. A framework for validating AI in precision medicine: considerations from the European ITFoC consortium. BMC Med Inform Decis Mak. 2021;21:274.
172. Ghasemi M, Samadi M, Soleimanian E, Chau KW. A comparative study of black-box and white-box data-driven methods to predict landfill leachate permeability. Environ Monit Assess. 2023;195:862.
173. Mey O, Neufeld D. Explainable AI algorithms for vibration data-based fault detection: use case-adadpted methods and critical evaluation. Sensors. 2022;22:9037.
174. Noseworthy PA, Attia ZI, Brewer LC, et al. Assessing and mitigating bias in medical artificial intelligence: the effects of race and ethnicity on a deep learning model for ECG analysis. Circ Arrhythm Electrocardiol. 2020;13:e007988.
175. De Rosa S, Brida M, Grapsa J, Dos Subira L, Bäck M, Chieffo A. Women in cardiology. Eur Heart J Open. 2023;3:oead122.
176. Abrams C. Google’s effort to prevent blindness shows AI challenges. Wall Street J. 2019;1:26. Available from: https://www.wsj.com/articles/googles-effort-to-prevent-blindness-hits-roadblock-11548504004 [Last accessed on 25 Apr 2024].
177. Hardesty L. Study finds gender and skin-type bias in commercial artificial-intelligence systems. 2018. Available from: https://news.mit.edu/2018/study-finds-gender-skin-type-bias-artificial-intelligence-systems-0212 [Last accessed on 5 Mar 2024].
