人工智能在冠心病临床诊疗中的应用与挑战:从影像学分析到多组学联合

doi:10.16352/j.issn.1001-6325.2025.02.0160

摘要/Abstract

摘要： 冠状动脉粥样硬化性心脏病(简称冠心病)是我国最常见的心血管疾病之一,患者数量持续增长,个性化和精准治疗面临诸多挑战。人工智能凭借其在处理和分析医疗数据方面的优势,通过整合临床信息、影像检查和各种组学分析,人工智能为临床医生提供精准的诊断和治疗建议,并在风险预测、诊断优化及个性化治疗策略制定中发挥重要作用。本文探讨人工智能在冠心病诊疗中的应用,分析其在风险预测、诊断优化和治疗决策中的贡献与面临的挑战,展望其在心血管医学领域的未来发展潜力。

关键词: 冠心病, 人工智能, 深度学习, 机器学习, 多组学联合

Abstract: Coronary heart disease (CHD) is one of the most prevalent cardiovascular diseases in China, with a continuously growing patient population, presenting numerous challenges for personalized and precise treatment. Artificial intelligence (AI), leveraging its advantages in processing and analyzing medical data, integrates clinical information, imaging examinations, and various omics analyses to provide clinicians with accurate diagnostic and treatment recommendations. AI plays a crucial role in risk prediction, diagnostic optimization, and the development of personalized treatment strategies. This article explores the applications of AI in the diagnosis and treatment of CHD, analyzing its contributions and challenges in risk prediction, diagnostic optimization, and treatment decision-making, while also envisioning its future developmental in the field of cardiovascular medicine.

Key words: coronary heart disease, artificial intelligence, deep learning, machine learning, multi-omics integration

中图分类号:

R541

陈文杰, 刘怡铭, 史雨晨, 柳景华. 人工智能在冠心病临床诊疗中的应用与挑战:从影像学分析到多组学联合[J]. 基础医学与临床, 2025, 45(2): 160-167.

CHEN Wenjie, LIU Yiming, SHI Yuchen, LIU Jinghua. Applications and challenges of artificial intelligence in the clinical management of coronary artery disease: from imaging analysis to multi-omics integration[J]. Basic & Clinical Medicine, 2025, 45(2): 160-167.

参考文献 53

[1]	国家心血管病中心, 中国心血管健康与疾病报告编写组. 中国心血管健康与疾病报告2023概要[J]. 中国循环杂志, 2024, 39: 625-660.
[2]	Al Kuwaiti A, Nazer K, Al-Reedy A, et al. A review of the role of artificial intelligence in healthcare[J]. J Pers Med, 2023,13:951. doi: 10.3390/jpm13060951.
[3]	Wu H, Xiao Z. A knowledge graph-based analytical model for mining clinical value of drug stress echocardiography for diagnosis, risk stratification and prognostic evaluation of coronary artery disease[J]. Int J Cardiol, 2023, 387:131107. doi: 10.1016/j.ijcard.2023.05.057.
[4]	Krittanawong C, Virk HUH, Bangalore S, et al. Machine learning prediction in cardiovascular diseases: a meta-analysis[J]. Sci Rep, 2020, 10:16057. doi: 10.1038/s41598-020-72685-1.
[5]	Cho H, Keenan G, Madandola OO, et al. Assessing the usability of a clinical decision support system: heuristic evaluation[J].JMIR Hum Factors, 2022, 9:e31758. doi: 10.2196/31758.
[6]	Sun X, Yin Y, Yang Q, et al. Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives[J]. Eur J Med Res, 2023, 28:242. doi: 10.1186/s40001-023-01065-y.
[7]	Kayvanpour E, Mansi T, Sedaghat-Hamedani F, et al.Towards personalized cardiology: multi-scale modeling of the failing heart[J].PLoS One,2015,10: e0134869.doi: 10.1371/journal.pone.0134869.
[8]	Katus H, Ziegler A, Ekinci O, et al.Early diagnosis of acute coronary syndrome[J].Eur Heart J,2017, 38:3049-3055.
[9]	Ghesu FC, Georgescu B, Grbic S, et al.Towards intelligent robust detection of anatomical structures in incomplete volumetric data[J].Med Image Anal,2018, 48:203-213.
[10]	Ghesu FC, Georgescu B, Zheng Y, et al.Multi-scale deep reinforcement learning for real-time 3D-landmark detection in CT scans[J].IEEE Trans Pattern Anal Mach Intell,2019, 41:176-189.
[11]	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[J].IEEE Trans Med Imaging,2008, 27:1668-1681.
[12]	Yang L, Georgescu B, Zheng Y, et al.Prediction based collaborative trackers (PCT): a robust and accurate approach toward 3D medical object tracking[J].IEEE Trans Med Imaging, 2011, 30:1921-1932.
[13]	Zhou SK, Guo F, Park J, et al.A probabilistic, hierarchical and discriminant framework for rapid and accurate detection of deformable anatomic structures[C]. 2007 IEEE 11th International Conference on Computer Vision, 2007:1-8,
[14]	Qazi M, Fung G, Krishnan S, et al.Automated heart wall motion abnormality detection from ultrasound images using Bayesian networks[C]. Proceedings of the 20th international joint conference on Artificial intelligence, 2007:519-525.
[15]	Kelm BM, Mittal S, Zheng Y, et al.Detection, grading and classification of coronary stenoses in computed tomography angiography[J].Med Image Comput Comput Assist Interv,2011,14:25-32.
[16]	Gaur S, Øvrehus KA, Dey D, et al.Coronary plaque quantification and fractional flow reserve by coronary computed tomography angiography identify ischaemia-causing lesions[J].Eur Heart J,2016, 37:1220-1227.
[17]	Dey D, Gaur S, Ovrehus KA, et al.Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study[J].Eur Radiol,2018, 28:2655-2664.
[18]	Lenstrup M, Kjaergaard J, Petersen CL, et al.Evaluation of left ventricular mass measured by 3D echocardiography using magnetic resonance imaging as gold standard[J].Scand J Clin Lab Invest,2006,66:647-657.
[19]	Wang YJ, Yang K, Wen Y, et al. Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging[J]. Nat Med, 2024,30:1471-1480.
[20]	Knott KD, Seraphim A, Augusto JB, et al. The prognostic significance of quantitative myocardial perfusion: an artificial intelligence-based approach using perfusion mapping[J].Circulation,2020,141:1282-1291.
[21]	Chen W, Liu J, Shi Y. Machine learning predictions of the adverse events of different treatments in patients with ischemic left ventricular systolic dysfunction[J]. Intern Emerg Med, 2024,19:1847-1857.
[22]	Papandrianos NI, Apostolopoulos ID, Feleki A, et al. Deep learning exploration for SPECT MPI polar map images classification in coronary artery disease[J]. Ann Nucl Med, 2022, 36: 823-833.
[23]	Wang T, Lei Y, Tang H, et al. A learning-based auto-matic segmentation and quantification method on left ventricle in gated myocardial perfusion SPECT imaging: a feasibility study[J]. J Nucl Cardiol, 2020, 27: 976-987.
[24]	Josselyn N, MacLean MT, Jean C, et al. Classification of myocardial 18F-FDG PET uptake patterns using deep learning[J]. Radiol Artif Intell, 2021, 3: e200148.doi: 10.1148/ryai.2021200148.
[25]	Kakadiaris IA, Vrigkas M, Yen AA, et al. Machine learning outperforms ACC / AHA CVD risk calculator in MESA[J]. J Am Heart Assoc, 2018,7:e009476. doi: 10.1161/JAHA.118.009476.
[26]	中华医学会超声医学分会超声心动图学组.负荷超声心动图规范化操作指南[J].中国医学影像技术, 2017, 33:632-638.
[27]	Huang MS, Wang CS, Chiang JH, et al. Automated recognition of regional wall motion abnormalities through deep neural network interpretation of transthoracic echocardiography[J]. Circulation, 2020,142:1510-1520.
[28]	Al'Aref SJ, Maliakal G, Singh G, et al. Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry[J]. Eur Heart J, 2020,41:359-367.
[29]	Nakanishi R, Sankaran S, Grady L, et al.Automated estimation of image quality for coronary computed tomographic angiography using machine learning[J].Eur Radiol,2018, 28:4018-4026..
[30]	Yang S, Koo BK, Hoshino M, et al. CT Angiographic and plaque predictors of functionally significant coronary disease and outcome using machine learning[J]. JACC Cardiovasc Imaging, 2021,14:629-641.
[31]	Zreik M, Lessmann N, van Hamersvelt RW, et al.Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis[J].Med Image Anal,2018,44:72-85.
[32]	Tesche C, De Cecco CN, Baumann S, et al.Coronary CT angiography-derived fractional flow reserve: machine learning algorithm versus computational fluid dynamics modeling[J].Radiology, 2018,288:64-72.
[33]	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[J].Circ Cardiovasc Imaging,2018,11: e007217. doi: 10.1161/CIRCIMAGING.117.007217.
[34]	Tamarappoo BK, Lin A, Commandeur F, et al. Machine learning integration of circulating and imaging biomarkers for explainable patient-specific prediction of cardiac events: a prospective study[J]. Atherosclerosis,2021,318:76-82.
[35]	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].J Nucl Cardiol,2013,20:553-562.
[36]	Lin S, Li Z, Fu B, et al. Feasibility of using deep learning to detect coronary artery disease based on facial photo[J]. Eur Heart J, 2020,41:4400-4411.
[37]	Cheung CY, Xu D, Cheng CY, et al. A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre[J].Nat Biomed Eng,2021,5:498-508.
[38]	Rim TH, Lee CJ, Tham YC, et al. Deep-learning-based cardiovascular risk stratifcation using coronary artery calcium scores predicted from retinal photographs[J]. Lancet Digit Health, 2021,3:e306-316.
[39]	Raghunath S, Ulloa Cerna AE, Jing L, et al. Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network[J].Nat Med,2020, 26:886-891.
[40]	Zeleznik R, Foldyna B, Eslami P, et al. Deep convolutional neural networks to predict cardiovascular risk from computed tomography[J].Nat Commun,2021,12:715.doi: 10.1038/s41467-021-20966-2.
[41]	Eisenberg E, McElhinney PA, Commandeur F, et al. Deep learning-based quantification of epicardial adipose tissue volume and attenuation predicts major adverse cardiovascular events in asymptomatic subjects[J].Circ Cardiovasc Imaging,2020,13:e009829. doi: 10.1161/CIRCIMAGING.119.009829. Epub 2020 Feb 17.
[42]	de Souza ESCG, Buginga GC, de Souza ESEA, et al. Prediction of mortality in coronary artery disease: role of machine learning and maximal exercise capacity[J].Mayo Clin Proc,2022,97:1472-1482.
[43]	Arsanjani R, Dey D, Khachatryan T, et al.Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population[J].J Nucl Cardiol,2015,22:877-884.
[44]	Betancur JCF, Motlagh M, Sharir T, et al.Deep learning for prediction of obstructive disease from fast myocardial perfusion SPECT: a multicenter study[J].J Am Coll Cardiol Img,2018,11:1654-1663.
[45]	Min HS, Ryu D, Kang SJ, et al. Prediction of coronary stent underexpansion by pre-procedural intravascular ultrasound-based deep learning[J].JACC Cardiovasc Interv,2021,14:1021-1029.
[46]	Pattarabanjird T, Cress C, Nguyen A, et al. A machine learning model utilizing a novel SNP shows enhanced prediction of coronary artery disease severity[J]. Genes, 2020,11:1446. doi: 10.3390/genes11121446.
[47]	Dogan MV, Grumbach IM, Michaelson JJ, et al. Integrated genetic and epigenetic prediction of coronary heart disease in the Framingham Heart Study[J]. PLoS One, 2018,13:e0190549. doi: 10.1371/journal.pone.0190549.
[48]	Antonopoulos AS, Margaritis M, Coutinho P, et al. Reciprocal effects of systemic infammation and brain natriuretic peptide on adiponectin biosynthesis in adipose tissue of patients with ischemic heart disease[J]. Arterioscler Thromb Vasc Biol, 2014,34:2151-2159.
[49]	Antonopoulos A, Sanna F, Sabharwal N, et al. Detecting human coronary infammation by imaging perivascular fat[J]. Sci Transl Med, 2017, 9:eaal2658. doi: 10.1126/scitranslmed.aal2658.
[50]	Oikonomou EK, Marwan M, Desai MY, et al. Non-invasive detection of coronary infammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data[J]. Lancet, 2018,392:929-939.
[51]	Nurmohamed NS, Belo Pereira JP, Hoogeveen RM, et al. Targeted proteomics improves cardiovascular risk prediction in secondary prevention[J]. Eur Heart J, 2022,43:1569-1577.
[52]	Mazidi M, Wright N, Yao P, et al. Plasma proteomics to identify drug targets for ischemic heart disease[J]. J Am Coll Cardiol, 2023, 82:1906-1920.
[53]	Ouwerkerk W, Belo Pereira JP, Maasland T, et al. Multiomics analysis provides novel pathways related to progression of heart failure[J]. J Am Coll Cardiol, 2023, 82:1921-1931.

编辑推荐 0

Metrics

阅读次数

全文

HTML			PDF

最新录用	在线预览	正式出版	最新录用	在线预览	正式出版
0	0	0	0	0	9

	来源	本网站

	次数	9
	比例	100%

摘要

最新录用	在线预览	正式出版

0	0	87

	来源	本网站

	次数	87
	比例	100%