Application of artificial intelligence in cardiovascular disease research and clinical practice

doi:10.16352/j.issn.1001-6325.2022.11.1644

Abstract

Abstract: This paper briefly described the application of artificial intelligence (AI) in disease management and clinical research for diagnosis, prediction, treatment, and basic research of cardiovascular diseases (CVDs) and analyzed challenges in this field.The review highlighted extensive progress of AI technology application in the diagnosis and prediction of CVDs. For example, AI combined with ECG and cardiovascular imaging achieved an accurate diagnosis. Moreover, AI may integrate imaging data with other clinical data and so supported early screening and predicting of coronary artery disease, congenital heart disease, heart failure, and other CVDs risk. This paper also pointed out that AI-assisted cardiovascular interventional therapy and clinical decision-making are still on the way of development and AI-assisted multi-omics research is far from clinical application, which is the main direction of future research. In this paper, the problems faced by AI in the diagnosis, prediction, treatment, and basic research of CVDs were summarized and the application prospects of AI in the cardiovascular field have been prospected.

Key words: artificial intelligence(AI), cardiovascular diseases(CVDs), clinical application

CLC Number:

WU Yue-heng, YU Xi-yong. Application of artificial intelligence in cardiovascular disease research and clinical practice[J]. Basic & Clinical Medicine, 2022, 42(11): 1644-1649.

References 22

[1]	Ranka S, Reddy M, Noheria A. Artificial intelligence in cardiovascular medicine[J]. Curr Opin Cardiol, 2021, 36: 26-35.
[2]	Sermesant M, Delingette H, Cochet H, et al. Applica-tions of artificial intelligence in cardiovascular imaging[J]. Nat Rev Cardiol, 2021, 18: 600-609.
[3]	Attia ZI, Noseworthy PA, Lopez-Jimenez F, et al. An artificial intelligence-enabled ecg algorithm for the identification of patients with atrial fibrillation during sinus rhythm: A retrospective analysis of outcome prediction[J]. Lancet, 2019, 394: 861-867.
[4]	Matsumoto T, Kodera S, Shinohara H, et al. Erratum: Diagnosing heart failure from chest x-ray images using deep learning[J]. Int Heart J, 2020, 61: 1088.
[5]	Arnaout R, Curran L, Zhao Y, et al. An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease[J]. Nat Med, 2021, 27: 882-891.
[6]	Gurpreet S, Vendargon SJ, Syed Rasul SH. Coronary artery bypass graft surgery in a young female with systemic lupus erythematosus and its operative challenges: A case report[J]. Med J Malaysia, 2019, 74: 549-550.
[7]	Mathew RC, Gottbrecht M, Salerno M. Computed tomography fractional flow reserve to guide coronary angio-graphy and intervention[J]. Interv Cardiol Clin, 2018, 7: 345-354.
[8]	Liu H, Wu J, Miller E J, et al. Diagnostic accuracy of stress-only myocardial perfusion spect improved by deep learning[J]. Eur J Nucl Med Mol Imaging, 2021, 48: 2793-2800.
[9]	Ambale-Venkatesh B, Lima JAC. Human-in-the-loop artificial intelligence in cardiac mri[J]. Radiology, 2022: 221132.
[10]	Chen J, Yang ZG, Xu HY, et al. Assessment of left ventricular myocardial deformation by cardiac mri strain imaging reveals myocardial dysfunction in patients with primary cardiac tumors[J]. Int J Cardiol, 2018, 253: 176-182.
[11]	Naushad SM, Hussain T, Indumathi B, et al. Machine learning algorithm-based risk prediction model of coronary artery disease[J]. Mol Biol Rep, 2018, 45: 901-910.
[12]	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]. J Thorac Cardiovasc Surg, 2019, 158: 234-243 e233.
[13]	Diller GP, Kempny A, Babu-Narayan SV, et al. Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: Data from a single tertiary centre including 10 019 patients[J]. Eur Heart J, 2019, 40: 1069-1077.
[14]	De Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease[J]. Nat Med, 2018, 24: 1342-1350.
[15]	Cikes M, Sanchez-Martinez S, Claggett B, et al. Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy[J]. Eur J Heart Fail, 2019, 21: 74-85.
[16]	Pimor A, Galli E, Vitel E, et al. Predictors of post-operative cardiovascular events, focused on atrial fibrillation, after valve surgery for primary mitral regurgitation[J]. Eur Heart J Cardiovasc Imaging, 2019, 20: 177-184.
[17]	Alamgir A, Mousa O, Shah Z. Artificial intelligence in predicting cardiac arrest: Scoping review[J]. JMIR Med Inform, 2021, 9: e30798.
[18]	Cresswell K, Callaghan M, Khan S, et al. Investigating the use of data-driven artificial intelligence in computer-ised decision support systems for health and social care: A systematic review[J]. Health Informatics J, 2020, 26: 2138-2147.
[19]	Sardar P, Abbott JD, Kundu A, et al. Impact of artificial intelligence on interventional cardiology: From decision-making aid to advanced interventional procedure assistance[J]. JACC Cardiovasc Interv, 2019, 12: 1293-1303.
[20]	Harper AR, Goel A, Grace C, et al. Common genetic variants and modifiable risk factors underpin hypertrophic cardiomyopathy susceptibility and expressivity[J]. Nat Genet, 2021, 53: 135-142.
[21]	Jung S, Song SW, Lee S, et al. Metabolic phenotyping of human atherosclerotic plaques: Metabolic alterations and their biological relevance in plaque-containing aorta[J]. Atherosclerosis, 2018, 269: 21-28.
[22]	Castagno S, Khalifa M. Perceptions of artificial intelli-gence among healthcare staff: A qualitative survey study[J]. Front Artif Intell, 2020, 3: 578983.