人工智能在老年心血管疾病中的应用研究进展

doi:10.3969/j.issn.1003-9198.2025.06.018

摘要/Abstract

摘要： 心血管疾病患病率在老年人群中逐年增加,已成为城乡居民的主要死亡原因,给公共卫生系统带来巨大的压力。近年来,人工智能技术在心血管疾病的诊断、治疗和管理中展现出巨大的应用潜力,其通过机器学习和深度学习技术可以有效分析复杂数据集,推动心电图分析、影像诊断、面部特征分析等领域的进步,帮助精准预测疾病风险和评估心血管疾病的预后。本文综述了人工智能在老年心血管疾病中的应用现状与研究进展,探讨了人工智能在老年心血管疾病诊断、风险评估、监测等方面的潜力。未来,人工智能技术将不断克服挑战,为老年心血管疾病的筛查、精准治疗和个性化管理提供更加有力的支持。

关键词: 人工智能, 老年人, 心血管疾病

Abstract: The prevalence of cardiovascular disease (CVD) in the elderly has been increasing year by year, becoming a leading cause of death among both urban and rural residents, placing enormous pressure on the public health system. Artificial intelligence (AI) technology has shown immense potential in the diagnosis, treatment, and management of CVD. Through machine learning (ML) and deep learning (DL) techniques, AI can effectively analyze complex datasets, driving progress in areas such as electrocardiogram analysis, imaging diagnostics, and facial feature analysis, helping to accurately predict disease risk and assess CVD prognosis. This study reviews the current status and research progress of AI applications in geriatric cardiovascular diseases, exploring its potential in the diagnosis, risk assessment, and disease monitoring of CVD in the elderly. In the future, AI technology will provide stronger support for the early screening, precise treatment, and personalized management of CVD in the elderly as it continues to overcome challenges.

Key words: artificial intelligence, aged, cardiovascular disease

中图分类号:

TP18
R54

朱相瑜, 杭乐佳, 王宇, 吴予一, 赵一菁, 朱蓓, 黄菁菁. 人工智能在老年心血管疾病中的应用研究进展[J]. 实用老年医学, 2025, 39(6): 621-625.

ZHU Xiangyu, HANG Lejia, WANG Yu, WU Yuyi, ZHAO Yijing, ZHU Bei, HUANG Jingjing. Advances in artificial intelligence for cardiovascular disease in the elderly[J]. Practical Geriatrics, 2025, 39(6): 621-625.

参考文献

[1] 刘明波, 何新叶, 杨晓红, 等. 《中国心血管健康与疾病报告2023》要点解读[J]. 中国全科医学, 2025, 28(1):20-38.
[2] 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(7):625-660.
[3] MORAN A, GU D, ZHAO D, et al. Future cardiovascular disease in China: Markov model and risk factor scenario projections from the coronary heart disease policy model-China[J]. Circ Cardiovasc Qual Outcomes, 2010, 3(3): 243-252.
[4] SINGH M, KUMAR A, KHANNA N N, et al. Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review[J]. EClinicalMedicine, 2024, 73: 102660.
[5] ZHU H, CHENG C, YIN H, et al. Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study[J]. Lancet Digit Health, 2020, 2(7): e348-e357.
[6] ATTIA Z I, NOSEWORTHY P A, 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(10201): 861-867.
[7] RAGHUNATH A, NGUYEN D D, SCHRAM M, et al. Artificial intelligence-enabled mobile electrocardiograms for event prediction in paroxysmal atrial fibrillation[J]. Cardiovasc Digit Health J, 2023, 4(1): 21-28.
[8] HARMON D M, MANGOLD K, SUAREZ A B, et al. Postdevelopment performance and validation of the artificial intelligence-enhanced electrocardiogram for detection of cardiac amyloidosis[J]. JACC Adv, 2023, 2(8): 100612.
[9] STAMATE E, PIRAIANU A I, CIOBOTARU O R, et al. Revolutionizing cardiology through artificial intelligence-big data from proactive prevention to precise diagnostics and cutting-edge treatment: a comprehensive review of the past 5 years[J]. Diagnostics: Basel, 2024, 14(11): 1103.
[10] WANG S, PATEL H, MILLER T, et al. AI based CMR assessment of biventricular function: clinical significance of intervendor variability and measurement errors[J]. JACC Cardiovasc Imaging, 2022, 15(3): 413-427.
[11] ZHANG Q, BURRAGE M K, LUKASCHUK E, et al. Toward replacing late gadolinium enhancement with artificial intelligence virtual native enhancement for gadolinium-free cardiovascular magnetic resonance tissue characterization in hypertrophic cardiomyopathy[J]. Circulation, 2021, 144(8): 589-599.
[12] NARANG A, BAE R, HONG H, et al. Utility of a deep-learning algorithm to guide novices to acquire echocardiograms for limited diagnostic use[J]. JAMA Cardiol, 2021, 6(6): 624-632.
[13] ZHANG J, GAJJALA S, AGRAWAL P, et al. Fully automated echocardiogram interpretation in clinical practice[J]. Circulation, 2018, 138(16): 1623-1635.
[14] KOTANIDIS C P, ANTONIADES C. Selfies in cardiovascular medicine: welcome to a new era of medical diagnostics[J]. Eur Heart J, 2020, 41(46): 4412-4414.
[15] 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(46): 4400-4411.
[16] XING W, SHI Y, WU C, et al. Predicting blood pressure from face videos using face diagnosis theory and deep neural networks technique[J]. Comput Biol Med, 2023, 164: 107112.
[17] ZHOU Y, CHIA M A, WAGNER S K, et al. A foundation model for generalizable disease detection from retinal images[J]. Nature, 2023, 622(7981): 156-163.
[18] DU T, XIE L, LIU X, et al. TCT-235 intelligent recognition of coronary angiography by deep learning technology: a novel computer-aided diagnostic system[J]. J Am Coll Cardiol, 2018, 72(13 Supplement): B98.
[19] FEARON W F, ACHENBACH S, ENGSTROM T, et al. Accuracy of fractional flow reserve derived from coronary angiography[J]. Circulation, 2019, 139(4): 477-484.
[20] HASIMBEGOVIC E, PAPP L, GRAHOVAC M, et al. A sneak-peek into the physician’s brain: a retrospective machine learning-driven investigation of decision-making in TAVR versus SAVR for young high-risk patients with severe symptomatic aortic stenosis[J]. J Pers Med, 2021, 11(11): 1062.
[21] KOTANIDIS C P, AKAWI N, THOMAS S, et al. A novel arterial redox-specific machine learning-derived radiomic signature of perivascular adipose tissue predicts cardiac mortality from routine CCTA[J]. Eur Heart J, 2020, 41(Supplement_2): ehaa946.1372.
[22] KOO B K, YANG S, JUNG J W, et al. Artificial intelligence-enabled quantitative coronary plaque and hemodynamic analysis for predicting acute coronary syndrome[J]. JACC Cardiovasc Imaging, 2024, 17(9): 1062-1076.
[23] VENKAT V, ABDELHALIM H, DEGROAT W, et al. Investigating genes associated with heart failure, atrial fibrillation, and other cardiovascular diseases, and predicting disease using machine learning techniques for translational research and precision medicine[J]. Genomics, 2023, 115(2): 110584.
[24] MCBANE R D 2nd, MURPHREE D H, LIEDL D, et al. Artificial intelligence of arterial Doppler waveforms to predict major adverse outcomes among patients evaluated for peripheral artery disease[J]. J Am Heart Assoc, 2024, 13(3): e031880.
[25] RAHEEM A, WAHEED S, KARIM M, et al. Prediction of major adverse cardiac events in the emergency department using an artificial neural network with a systematic grid search[J]. Int J Emerg Med, 2024, 17(1): 4.
[26] KOLK M Z H, RUIPÉREZ-CAMPILLO S, WILDE A A M, et al. Prediction of sudden cardiac death using artificial intelligence: current status and future directions[J]. Heart Rhythm, 2024: S1547-5271(24)03293-4.
[27] HERZOG L, ILAN BER R, HOROWITZ-KUGLER Z, et al. Causal deep neural network-based model for first-line hypertension management[J]. Mayo Clin Proc Digit Health, 2023, 1(4): 632-640.