人工智能在心血管疾病研究和临床中的应用

doi:10.16352/j.issn.1001-6325.2022.11.1644

基础医学与临床 ›› 2022, Vol. 42 ›› Issue (11): 1644-1649.doi: 10.16352/j.issn.1001-6325.2022.11.1644

• 特邀专题：人工智能在医学中的应用 • 上一篇下一篇

人工智能在心血管疾病研究和临床中的应用

吴岳恒¹, 余细勇^2*

1.广东省人民医院/广东省医学科学院广东省华南结构性心脏病重点实验室,广东广州 510080;
2.广州医科大学药学院国家药监局胸腔疾病药物临床研究与评价重点实验室,广东广州 511436

收稿日期:2022-07-15 修回日期:2022-08-25 出版日期:2022-11-05 发布日期:2022-11-01
通讯作者: ^* yuxycn@aliyun.com

Application of artificial intelligence in cardiovascular disease research and clinical practice

WU Yue-heng¹, YU Xi-yong^2*

1. Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou 510080;
2. NMPA and State Key Laboratory of Thoracic Disease, School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou 511436, China

Received:2022-07-15 Revised:2022-08-25 Online:2022-11-05 Published:2022-11-01
Contact: ^* yuxycn@aliyun.com

摘要/Abstract

摘要： 本文从心血管疾病的诊断、预测、治疗和基础研究等多方面简述了人工智能(AI)在心血管疾病研究和临床中的应用,回顾了AI在这些领域的应用,分析了其面临的困难和未来前景。AI在心血管疾病的诊断和预测领域已经取得广泛进展,如AI结合心电图和心血管影像数据可实现精准诊断,AI联合心血管影像数据和其他临床数据可实现冠状动脉疾病、先天性心脏病、心力衰竭等心血管疾病的早期筛查和风险预测,但其适用范围仍有待进一步观察。此外,AI辅助的心血管介入治疗和临床决策等尚处于开发阶段,AI辅助的多组学研究也远未达到临床应用水平,是未来的主要研究方向。在此基础上,总结了AI在心血管疾病的诊断、预测、治疗和基础研究中所面临的问题,并对AI在心血管领域的应用前景进行了展望。

关键词: 人工智能, 心血管疾病, 临床应用

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

中图分类号:

吴岳恒, 余细勇. 人工智能在心血管疾病研究和临床中的应用[J]. 基础医学与临床, 2022, 42(11): 1644-1649.

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.

参考文献 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.

人工智能在心血管疾病研究和临床中的应用

Application of artificial intelligence in cardiovascular disease research and clinical practice

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