Medicine-Engineering Interdisciplinary

Applications of Artificial Intelligence in Cardiac Electrophysiology and Clinical Diagnosis with Magnetic Resonance Imaging and Computational Modeling Techniques

  • 詹何庆1,韩贵来1,魏传安1,李治群2
Expand
  • (1. College of Biomedical Information and Engineering, Hainan Medical University, Haikou 571199, China; 2. Radiology Department, The First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou 570102, China)

Accepted date: 2022-10-10

  Online published: 2025-01-28

Abstract

The underlying electrophysiological mechanisms and clinical treatments of cardiovascular diseases, which are the most common cause of morbidity and mortality worldwide, have gotten a lot of attention and been widely explored in recent decades. Along the way, techniques such as medical imaging, computing modeling, and artificial intelligence (AI) have always played significant roles in above studies. In this article, we illustrated the applications of AI in cardiac electrophysiological research and disease prediction. We summarized general principles of AI and then focused on the roles of AI in cardiac basic and clinical studies incorporating magnetic resonance imaging and computing modeling techniques. The main challenges and perspectives were also analyzed.

Cite this article

詹何庆1,韩贵来1,魏传安1,李治群2 . Applications of Artificial Intelligence in Cardiac Electrophysiology and Clinical Diagnosis with Magnetic Resonance Imaging and Computational Modeling Techniques[J]. Journal of Shanghai Jiaotong University(Science), 2025 , 30(1) : 53 -65 . DOI: 10.1007/s12204-023-2628-5

References

[1] GOLDMAN M R, BRADY T J, PYKETT I L, et al. Quantification of experimental myocardial infarction using nuclear magnetic resonance imaging and paramagnetic ion contrast enhancement in excised canine hearts [J]. Circulation, 1982, 66(5): 1012-1016.
[2] WOLFE C L. Assessment of myocardial ischemia and infarction by contrast enhanced magnetic resonance imaging [J]. Cardiology Clinics, 1989, 7(3): 685-696.
[3] FILIPCHUK N G, PESHOCK R M, MALLOY C R, et al. Detection and localization of recent myocardial infarction by magnetic resonance imaging [J]. The American Journal of Cardiology, 1986, 58(3): 214- 219.
[4] MACHANAHALLI BALAKRISHNA A, ISMAYL M, THANDRA A, et al. Diagnostic value of cardiac magnetic resonance imaging and intracoronary optical coherence tomography in patients with a working diagnosis of myocardial infarction with non-obstructive coronary arteries - A systematic review and metaanalysis [J]. Current Problems in Cardiology, 2023, 48(6): 101126.
[5] LINTINGRE P F, NIVET H, CLEMENT GUINAUDEAU S, et al. High-resolution late gadolinium enhancement magnetic resonance for the diagnosis of myocardial infarction with nonobstructed coronay arteries [J]. JACC : Cardiovascular Imaging, 2020, 13(5): 1135-1148.
[6] TOUPIN S, PEZEL T, BUSTIN A, et al. Wholeheart high-resolution late gadolinium enhancement: Techniques and clinical applications [J]. Journal of Magnetic Resonance Imaging, 2022, 55(4): 967-987.
[7] HAUSVATER A, PASUPATHY S, TORNVALL P, et al. ST-segment elevation and cardiac magnetic resonance imaging findings in myocardial infarction with non-obstructive coronary arteries [J]. International Journal of Cardiology, 2019, 287: 128-131.
[8] RIBEIRO J M, DE JAEGERE P P. Artificial intelligence in cardiovascular medicine - are we ready? [J]. Trends in Cardiovascular Medicine, 2022.
[9] RANKA S, REDDY M, NOHERIA A. Artificial intelligence in cardiovascular medicine [J]. Current Opinion in Cardiology, 2020, 36(1): 26-35.
[10] NYG?ARDS M E, HULTING J. An automated system for ECG monitoring [J]. Computers and Biomedical Research, 1979, 12(2): 181-202.
[11] FRANKEL P, ROTHMEIER J, JAMES D, et al. A computerized system for ECG monitoring [J]. Computers and Biomedical Research, 1975, 8(6): 560-567.
[12] YANOWITZ F, KINIAS P, RAWLING D, et al. Accuracy of a continuous real-time ECG dysrhythmia monitoring system [j]. Circulation, 1974, 50(1): 65-72.
[13] PARK J, AN J, KIM J, et al. Study on the use of standard 12-lead ECG data for rhythm-type ECG classification problems [J]. Computer Methods and Programs in Biomedicine, 2022, 214: 106521.
[14] KASHOU A H, KO W Y, ATTIA Z I, et al. A comprehensive artificial intelligence-enabled electrocardiogram interpretation program [J]. Cardiovascular Digital Health Journal, 2020, 1(2): 62-70.
[15] SMITH S W, 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 [J]. IJC Heart & Vasculature, 2019, 25: 100423.
[16] HANNUN A Y, RAJPURKAR P, HAGHPANAHI M, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network [J]. Nature Medicine, 2019,25(1): 65-69.
[17] RIBEIRO A H, RIBEIRO M H, PAIXAO G M M, et al. Author Correction: Automatic diagnosis of the 12-lead ECG using a deep neural network [J]. Nature Communications, 2020, 11: 2227.
[18] ATTIA Z I, KAPA S, LOPEZ-JIMENEZ F, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram [J]. Nature Medicine, 2019, 25(1): 70-74.
[19] ADEDINSEWO D, CARTER R, ATTIA Z I, et al. Application of an artificial intelligence-enabled ECG algorithm to identify patients with left ventricular systolic dysfunction presenting to the emergency room with dyspnea [J]. Journal of the American College of Cardiology, 2020, 75(11): 3598.
[20] LEE H, SHIN S Y, SEO M, et al. Prediction of ventricular tachycardia one hour before occurrence using artificial neural networks [J]. Scientific Reports, 2016, 6: 32390.
[21] DOSTE R, LOZANO M, JIMENEZ-PEREZ G, et al. Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias [J]. Frontiers in Physiology, 2022, 13: 909372.
[22] ATTIA Z I, NOSEWORTHY P A, LOPEZJIMENEZ 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]. The Lancet, 2019, 394(10201):861-867.
[23] EBRAHIMZADEH E, KALANTARI M, JOULANI M, et al. Prediction of paroxysmal Atrial Fibrillation: A machine learning based approach using combine feature vector and mixture of expert classification on HRV signal [J]. Computer Methods and Programs in Biomedicine, 2018, 165: 53-67.
[24] JEKOVA I, CHRISTOV I, KRASTEVA V. Atrioventricular synchronization for detection of atrial fibrillation and flutter in one to twelve ECG leads using a dense neural network classifier [J]. Sensors, 2022, 22(16): 6071.
[25] KO W Y, SIONTIS K C, ATTIA Z I, et al. Detection of hypertrophic cardiomyopathy using a convolutional neural network-enabled electrocardiogram [J]. Journal of the American College of Cardiology, 2020, 75(7): 722-733.
[26] TISON G H, ZHANG J, DELLING F N, et al. Automated and interpretable patient ECG profiles for disease detection, tracking, and discovery [J]. Circulation Cardiovascular Quality and Outcomes, 2019, 12(9): e005289.
[27] GALLOWAY C D, VALYS A V, SHREIBATI J B, et al. Development and validation of a deep-learning model to screen for hyperkalemia from the electrocardiogram [J]. JAMA Cardiology, 2019, 4(5): 428.
[28] TOKODI M, SCHWERTNER W R, KOVACS A, et al. Machine learning-based mortality prediction of patients undergoing cardiac resynchronization therapy: the SEMMELWEIS-CRT score [J]. European Heart Journal, 2020, 41(18): 1747-1756.
[29] CIKES M, SANCHEZ-MARTINEZ S, CLAGGETT B, et al. Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy [J]. European Journal of Heart Failure, 2019, 21(1): 74-85.
[30] FEENY A K, RICKARD J, PATEL D, et al. Machine learning prediction of response to cardiac resynchronization therapy: Improvement versus current guidelines [J]. Circulation: Arrhythmia and Electrophysiology, 2019, 12(7): e007316.
[31] LEVY A E, BISWAS M, WEBER R, et al. Applications of machine learning in decision analysis for dose management for dofetilide [J]. PLoS One, 2019, 14(12): e0227324.
[32] ATTIA Z I, SUGRUE A, ASIRVATHAM S J, et al. Noninvasive assessment of dofetilide plasma concentration using a deep learning (neural network) analysis of the surface electrocardiogram: A proof of concept study [J]. PLoS One, 2018, 13(8): e0201059.
[33] SIMJANOSKA M, GJORESKI M, GAMS M, et al. Non-invasive blood pressure estimation from ECG using machine learning techniques [J]. Sensors, 2018, 18(4): 1160.
[34] ATTIA ZACHI I, FRIEDMAN PAUL A, NOSEWORTHY PETER A, et al. Age and sex estimation using artificial intelligence from standard 12 lead ECGs [J]. Circulation Arrhythmia and Electrophysiology, 2019, 12(9): e007284.
[35] DRAELOS R L, EZEKIAN J E, ZHUANG F, et al. Genesis: Gene-specific machine learning models for variants of uncertain significance found in catecholaminergic polymorphic ventricular Tachycardia and Long QT syndrome-associated genes [J]. Circulation: Arrhythmia and Electrophysiology, 2022, 15(4): e010326.
[36] SUNDARAM L, GAO H, PADIGEPATI S R, et al. Predicting the clinical impact of human mutation with deep neural networks [J]. Nature Genetics, 2018, 50(8): 1161-1170.
[37] GOROSPE G, ZHU R J, MILLROD M A, et al. Automated grouping of action potentials of human embryonicstem cell-derived cardiomyocytes [J]. IEEE Transactions on Biomedical Engineering, 2014, 61(9): 2389-2395.
[38] ZHU R J, MILLROD M A, ZAMBIDIS E T, et al. Variability of action potentials within and among cardiac cell clusters derived from human embryonic stem cells [J]. Scientific Reports, 2016, 6: 18544.
[39] BEDBROOK C N, YANG K K, ROBINSON J E, et al. Machine learning-guided channelrhodopsin engineering enables minimally invasive optogenetics [J]. Nature Methods, 2019, 16(11): 1176-1184.
[40] SAHLI COSTABAL F, MATSUNO K, YAO J, et al. Machine learning in drug development: Characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification [J]. Computer Methods in Applied Mechanics and Engineering, 2019, 348: 313-333.
[41] KIM H, NAM H. hERG-Att: Self-attention-based deep neural network for predicting hERG blockers [J]. Computational Biology and Chemistry, 2020, 87:107286.
[42] SHARIFI M, BUZATU D, HARRIS S, et al. Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks[J]. BMC Bioinformatics, 2017, 18(14): 93-102.
[43] CRUCES P D, TORKAR D, ARINI P D. Dynamic features of cardiac vector as alternative markers of drug-induced spatial dispersion [J]. Journal of Pharmacological and Toxicological Methods, 2020, 104: 106894.
[44] AGHASAFARI P, YANG P C, KERNIK D C, et al. A deep learning algorithm to translate and classify cardiac electrophysiology [J]. eLife, 2021, 10: 68335.
[45] JUHOLA M, PENTTINEN K, JOUTSIJOKI H, et al. Analysis of drug effects on iPSC cardiomyocytes with machine learning [J]. Annals of Biomedical Engineering, 2021, 49(1): 129-138.
[46] LAWSON B A, BURRAGE K, BURRAGE P, et al. Slow recovery of excitability increases ventricular fibrillation risk as identified by emulation [J]. Frontiers in Physiology, 2018, 9: 1114.
[47] MULIMANI M K, ALAGESHAN J K, PANDIT R. Deep-learning-assisted detection and termination of spiral and broken-spiral waves in mathematical models for cardiac tissue [J]. Physical Review Research, 2020, 2(2): 023155.
[48] SAHLI COSTABAL F, YANG Y B, PERDIKARIS P, et al. Physics-informed neural networks for cardiacactivation mapping [J]. Frontiers in Physics, 2020, 8:42.
[49] NEUMANN D, MANSI T. Machine learning methods for robust parameter estimation [M]//Artificial intelligence for computational modeling of the heart. London: Academic Press, 2020: 161-181.
[50] DHAMALA J, BAJRACHARYA P, AREVALO H J, et al. Embedding high-dimensional Bayesian optimization via generative modeling: Parameter personalization of cardiac electrophysiological models [J]. Medical Image Analysis, 2020, 62: 101670.
[51] FERRER-ALBERO A, GODOY E J, LOZANO M, et al. Non-invasive localization of atrial ectopic beats by using simulated body surface P-wave integral maps [J]. PLoS One, 2017, 12(7): e0181263.
[52] RONEY C H, BEACH M L, MEHTA A M, et al. In silico comparison of left atrial ablation techniques that target the anatomical, structural, and electrical substrates of atrial fibrillation [J]. Frontiers in Physiology, 2020, 11: 572874.
[53] GIFFARD-ROISIN S, DELINGETTE H, JACKSON T, et al. Transfer learning from simulations on a reference anatomy for ECGI in personalized cardiac resynchronization therapy [J]. IEEE Transactions on Biomedical Engineering, 2019, 66(2): 343-353. [54] YILMAZ B, MACLEOD R S, PUNSKE B B, et al. Venous catheter based mapping of ectopic epicardial activation: Training data set selection for statistical estimation [J]. IEEE Transactions on Biomedical Engineering, 2005, 52(11): 1823-1831.
[55] PRAKOSA A, SERMESANT M, ALLAIN P, et al. Cardiac electrophysiological activation pattern estimation from images using a patient-specific database of synthetic image sequences [J]. IEEE Transactions on Biomedical Engineering, 2014, 61(2): 235-245.
[56] IRONI L, TENTONI S. Interplay of spatial aggregation and computational geometry in extracting diagnostic features from cardiac activation data [J]. Computer Methods and Programs in Biomedicine, 2012, 107(3): 456-467.
[57] COLLETTI P M. Multicenter, scan-rescan, human and machine learning CMR study to test generalizability and precision in imaging biomarker analysis [J]. Circulation: Cardiovascular Imaging, 2019, 12(10): e009214.
[58] AUGUSTO J B, DAVIES R H, BHUVA A N, et al. Diagnosis and risk stratification in hypertrophic cardiomyopathy using machine learning wall thickness measurement: A comparison with human test-retest performance [J]. The Lancet Digital Health, 2021,3(1): e20-e28.
[59] FAHMY A S, NEISIUS U, CHAN R H, et al. Threedimensional deep convolutional neural networks for automated myocardial scar quantification in hypertrophic cardiomyopathy: A multicenter multivendor study [J]. Radiology, 2020, 294(1): 52-60.
[60] RUIJSINK B, PUYOL-ANTON E, OKSUZ I, et al.Fully automated, quality-controlled cardiac analysis from CMR [J]. JACC : Cardiovascular Imaging, 2020,13(3): 684-695.
[61] ANKENBRAND M J, LOHR D, SCHLOTELBURGW, et al. Deep learning-based cardiac cine segmentation: Transfer learning application to 7T ultrahigh-field MRI [J]. Magnetic Resonance in Medicine, 2021,86(4): 2179-2191.
[62] ZHANG J, GAJJALA S, AGRAWAL P, et al. Fully automated echocardiogram interpretation in clinical practice [J]. Circulation, 2018, 138(16): 1623-1635.
[63] MACGREGOR R M, GUO A X, MASOOD M F, et al. Machine learning outcome prediction in dilated cardiomyopathy using regional left ventricular multiparametric strain [J]. Annals of Biomedical Engineering, 2021, 49(2): 922-932.
[64] SATRIANO A, AFZAL Y, SARIM AFZAL M, et al. Neural-network-based diagnosis using 3-dimensional myocardial architecture and deformation: Demonstration for the differentiation of hypertrophic cardiomyopathy [J]. Frontiers in Cardiovascular Medicine, 2020, 7: 584727.
[65] GOTO S, SOLANKI D, JOHN J, et al. Multinational federated learning approach to train ECG and echocardiogram models for hypertrophic cardiomyopathy detection [J]. Circulation, 2022, 146: 755-769.
[66] AVARD E, SHIRI I, HAJIANFAR G, et al. Noncontrast Cine Cardiac Magnetic Resonance image radiomics features and machine learning algorithms for myocardial infarction detection [J]. Computers in Biology and Medicine, 2022, 141: 105145.
[67] ZHANG N, YANG G A, GAO Z F, et al. Deep learning for diagnosis of chronic myocardial infarction on nonenhanced cardiac cine MRI [J]. Radiology, 2019,291(3): 606-617.
[68] MURAKI R, TERAMOTO A, SUGIMOTO K, et al. Automated detection scheme for acute myocardial infarction using convolutional neural network and long short-term memory [J]. PLoS One, 2022, 17(2): e0264002.
[69] HERNANDEZ-CASILLAS A, DEL-CANTO I, RUIZ-ESPANA S, et al. Detection and classification of myocardial infarction transmurality using cardiac MR image analysis and machine learning algorithms [C]//2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society. Glasgow: IEEE, 2022: 1686-1689.
[70] CORNHILL AIDAN K, STEVEN D, ALESSANDRO S, et al. Machine learning patient-specific prediction of heart failure hospitalization using cardiac MRIbased phenotype and electronic health Information [J]. Frontiers in Cardiovascular Medicine, 2022, 9:890904.
[71] ZHONG H, WU J Q, ZHAO W Y, et al. A selfsupervised learning based framework for automatic heart failure classification on cine cardiac magnetic resonance image [C]//2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society. Mexico: IEEE, 2021: 2887-2890.
[72] KERMANI S, GHELICH OGHLI M, MOHAMMADZADEH A, et al. NF-RCNN: Heart localization and right ventricle wall motion abnormality detection in cardiac MRI [J]. Physica Medica, 2020, 70: 65-74.
[73] AFSHIN M, BEN AYED I, PUNITHAKUMAR K, et al. Regional assessment of cardiac left ventricular myocardial function via MRI statistical features [J]. IEEE Transactions on Medical Imaging, 2014, 33(2): 481-494.
[74] LIN X, YANG F, CHEN Y, et al. Echocardiographybased AI detection of regional wall motion abnormalities and quantification of cardiac function in myocardial infarction [J]. Frontiers in Cardiovascular Medicine, 2022, 9: 903660.
[75] HUANG M S, WANG C S, CHIANG J, et al. Automated recognition of regional wall motion abnormalities through deep neural network interpretation of transthoracic echocardiography [J]. Circulation, 2020, 142: 1510-1520.
[76] PAPANDRIANOS N I, FELEKI A, PAPAGEORGIOU E I, et al. Deep learning-based automated diagnosis for coronary artery disease using SPECTMPI images [J]. Journal of Clinical Medicine, 2022, 11(13): 3918.
[77] ZHANG L, WAHLE A, CHEN Z, et al. Predicting locations of high-risk plaques in coronary arteries in patients receiving statin therapy [J]. IEEE Transactions on Medical Imaging, 2018, 37(1): 151-161.
[78] OTAKI Y, SINGH A, KAVANAGH P, et al. Clinical deployment of explainable artificial intelligence of SPECT for diagnosis of coronary artery disease [J]. JACC : Cardiovascular Imaging, 2022, 15(6): 1091- 1102.
[79] BETANCUR J, HU L H, COMMANDEUR F, et al. Deep learning analysis of upright-supine highefficiency SPECT myocardial perfusion imaging for prediction of obstructive coronary artery disease: Amulticenter study [J]. Journal of Nuclear Medicine, 2019, 60(5): 664-670.
[80] JUAREZ-OROZCO L E, KNOL R J J, SANCHEZCATASUS C A, et al. Machine learning in the integration of simple variables for identifying patients with myocardial ischemia [J]. Journal of Nuclear Cardiology, 2020, 27(1): 147-155.
[81] COENEN A, KIM Y H, 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 [J]. Circulation: Cardiovascular Imaging, 2018, 11(6): e007217.
[82] CHUN S H, SUH Y J, HAN K, et al. Deep learningbased reconstruction on cardiac CT yields distinct radiomic features compared to iterative and filtered back projection reconstructions [J]. Scientific Reports, 2022, 12: 15171.
[83] CHEN Y T, XIE W, ZHANG J W, et al. Myocardial segmentation of cardiac MRI sequences with temporal consistency for coronary artery disease diagnosis [J]. Frontiers in Cardiovascular Medicine, 2022, 9: 804442.
[84] BAJAJ R, EGGERMONT J, GRAINGER S J, et al. Machine learning for atherosclerotic tissue component classification in combined near-infrared spectroscopy intravascular ultrasound imaging: Validation against histology [J]. Atherosclerosis, 2022, 345: 15-25.
[85] JIN X, LI Y Z, YAN F, et al. Automatic coronary plaque detection, classification, and stenosis grading using deep learning and radiomics on computed tomography angiography images: A multi-center multivendor study [J]. European Radiology, 2022, 32(8):5276-5286.
[86] SUINESIAPUTRA A, MAUGER C A, AMBALEVENKATESH B, et al. Deep learning analysis of cardiac MRI in legacy datasets: Multi-ethnic study of atherosclerosis [J]. Frontiers in Cardiovascular Medicine, 2022, 8: 807728.
[87] WOJNARSKI C M, ROSELLI E E, IDREES J J, et al. Machine-learning phenotypic classification of bicuspid aortopathy [J]. The Journal of Thoracic and Cardiovascular Surgery, 2018, 155(2): 461-469.e4.
[88] NIZAR M, CHAN C, KHALIL A, et al. Real-time detection of aortic valve in echocardiography using convolutional neural networks [J]. Current Medical Imaging Reviews, 2020, 16(5): 584-591.
[89] Chinese Society of Pacing and Electrophysiology, Chinese Society of Arrhythmias, Atrial Fibrillation Center Union of China. Current knowledge and management of atrial fibrillation: Consensus of Chinese experts 2021 [J]. Chinese Journal of Cardiac Arrhythmias, 2022, 26(1): 15-88 (in Chinese).
[90] HUANG C X. Atrial fibrillation, what exactly do we know? [J]. Chinese Journal of Cardiac Arrhythmias, 2020, 24(1): 5-9 (in Chinese).
[91] ZAHID S, COCHET H, BOYLE P M, et al. Patientderived models link re-entrant driver localization in atrial fibrillation to fibrosis spatial pattern [J]. Cardiovascular Research, 2016, 110(3): 443-454.
[92] ROY A, VARELA M, ASLANIDI O. Image-based computational evaluation of the effects of atrial wall thickness and fibrosis on re-entrant drivers for atrial fibrillation [J]. Frontiers in Physiology, 2018, 9: 1352.
[93] SHI L Z, HENG R, LIU S M, et al. Effect of catheter ablation versus antiarrhythmic drugs on atrial fibrillation: A meta-analysis of randomized controlled trials [J]. Experimental and Therapeutic Medicine, 2015,10(2): 816-822.
[94] NEDIOS S, LINDEMANN F, HEIJMAN J, et al. Atrial remodeling and atrial fibrillation recurrence after catheter ablation [J]. Herz,2021, 46(4): 312-317.
[95] MARROUCHE N F, WILBER D, HINDRICKS G, et al. Association of atrial tissue fibrosis identified by delayed enhancement MRI and atrial fibrillation catheter ablation [J]. JAMA, 2014, 311(5): 498.
[96] HUANG W, ZHANG J. The ablation procedures and assessment of persistent atrial fibrillation [J]. Chinese Journal of Cardiac Pacing and Electrophysiology, 2021, 35(5): 477-479 (in Chinese).
[97] VARELA M, BISBAL F, ZACUR E, et al. Novel computational analysis of left atrial anatomy improves prediction of atrial fibrillation recurrence after ablation [J]. Frontiers in Physiology, 2017, 8: 68.
[98] BHALODIA R, GOPARAJU A, SODERGREN T, et al. Deep learning for end-to-end atrial fibrillation recurrence estimation [C]//2018 Computing in Cardiology Conference. Maastricht: IEEE, 2018: 1-4.
[99] BOYLE P M, ZGHAIB T, ZAHID S, et al. Computationally guided personalized targeted ablation of persistent atrial fibrillation [J]. Nature Biomedical Engineering, 2019, 3(11): 870-879.
[100] SHADE J K, ALI R L, BASILE D, et al. Preprocedure application of machine learning and mechanistic simulations predicts likelihood of paroxysmal atrial fibrillation recurrence following pulmonary vein isolation [J]. Circulation Arrhythmia and Electrophysiology, 2020, 13(7): e008213.
[101] Chinese Society of Pacing and Electrophysiology, Chinese Society of Arrhythmias. 2020 Chinese Society of Pacing and Electrophysiology (CSPE) /Chinese Society of Arrhythmias (CSA) expert consensus statement on ventricular arrhythmias (2016 update) [J]. Chinese Journal of Cardiac Arrhythmias, 2020, 24(3): 188-258 (in Chinese).
[102] Chinese Society of Pacing and Electrophysiology, Chinese Society of Arrhythmias. The interpretation of 2020 Chinese Society of Pacing and Electrophysiology (CSPE)/Chinese Society of Arrhythmias (CSA) expert consensus statement on ventricular arrhythmias [J]. Chinese Journal of Cardiac Arrhythmias,2020, 24(4): 348-350 (in Chinese).
[103] LIN G, NISHIMURA R A, GERSH B J, et al. Device complications and inappropriate implantable cardioverter defibrillator shocks in patients with hypertrophic cardiomyopathy [J]. Heart, 2009, 95(9):709-714.
[104] KRAMER C M, BILCHICK K C. Defibrillator or No defibrillator with CRT [J]. Journal of the American College of Cardiology, 2022, 79(7): 679-681.
[105] ALIS D, GULER A, YERGIN M, et al. Assessment of ventricular tachyarrhythmia in patients with hypertrophic cardiomyopathy with machine learning-based texture analysis of late gadolinium enhancement cardiac MRI [J]. Diagnostic and Interventional Imaging, 2020, 101(3): 137-146.
[106] OKADA DAVID R, JASON M, JONATHAN C, et al. Substrate spatial complexity analysis for the prediction of ventricular arrhythmias in patients with ischemic cardiomyopathy [J]. Circulation Arrhythmia and Electrophysiology, 2020, 13(4): e007975.
[107] MERLO M, CANNATA A, GOBBO M, et al. Evolving concepts in dilated cardiomyopathy [J]. European Journal of Heart Failure, 2018, 20(2): 228-239.
[108] JEFFERIES J L, TOWBIN J A. Dilated cardiomyopathy [J]. The Lancet, 2010, 375(9716): 752-762.
[109] HU R, LI R, YANG P C, et al. Advances in the clinical application of cardiac magnetic resonance in the diagnosis of left ventricular hypertrophy [J]. Chinese Journal of Magnetic Resonance Imaging, 2022, 13(5): 151-153 (in Chinese).
[110] LI S, FENG C, YU K, et al. Critical review of human cardiac magnetic resonance image super resolution reconstruction based on deep learning method [J]. Journal of Image and Graphics, 2022, 27(3): 704-721 (in Chinese).
[111] HEIJMAN J, SUTANTO H, CRIJNS H J G M, et al. Computational models of atrial fibrillation: Achievements, challenges, and perspectives for improving clinical care [J]. Cardiovascular Research, 2021, 117(7): 1682-1699.
[112] YI P, WANG K, HUANG C, et al. Adversarial attacks in artificial intelligence: A survey [J]. Journal of Shanghai Jiao Tong University, 2018, 52(10): 1298-1306 (in Chinese).
[113] ZIHNI E, MADAI V I, LIVNE M, et al. Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome [J]. PLoS One, 2020, 15(4): e0231166.
Outlines

/