• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

人工智能在心脏病学中的应用:概念、工具和挑战 - “马是奔跑的那个,你必须是骑师”。

Artificial Intelligence in Cardiology: Concepts, Tools and Challenges - "The Horse is the One Who Runs, You Must Be the Jockey".

机构信息

Universidade Federal Fluminense, Niterói, RJ, Brasil.

Universidade Federal Rural do Rio de Janeiro - Departamento de Tecnologias e Linguagens, Nova Iguaçu, RJ – Brazil

出版信息

Arq Bras Cardiol. 2020 Apr;114(4):718-725. doi: 10.36660/abc.20180431. Epub 2020 May 29.

DOI:10.36660/abc.20180431
PMID:32491009
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9744354/
Abstract

The recent advances at hardware level and the increasing requirement of personalization of care associated with the urgent needs of value creation for the patients has helped Artificial Intelligence (AI) to promote a significant paradigm shift in the most diverse areas of medical knowledge, particularly in Cardiology, for its ability to support decision-making and improve diagnostic and prognostic performance. In this context, the present work does a non-systematic review of the main papers published on AI in Cardiology, focusing on its main applications, potential impacts and challenges.

摘要

硬件水平的最新进展和与患者创造价值的迫切需求相关的个性化护理的日益增长的要求,帮助人工智能(AI)在医学知识的最广泛领域中推动了重大的范式转变,特别是在心脏病学领域,因为它能够支持决策并提高诊断和预后性能。在这种情况下,本工作对心脏病学中关于人工智能的主要论文进行了非系统性回顾,重点关注其主要应用、潜在影响和挑战。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/9744354/b7457d384826/0066-782X-abc-114-04-0718-gf02-en.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/9744354/e5c3a6732d84/0066-782X-abc-114-04-0718-gf01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/9744354/8bb9c7cb01b4/0066-782X-abc-114-04-0718-gf02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/9744354/45e4bfb4e12f/0066-782X-abc-114-04-0718-gf01-en.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/9744354/b7457d384826/0066-782X-abc-114-04-0718-gf02-en.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/9744354/e5c3a6732d84/0066-782X-abc-114-04-0718-gf01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/9744354/8bb9c7cb01b4/0066-782X-abc-114-04-0718-gf02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/9744354/45e4bfb4e12f/0066-782X-abc-114-04-0718-gf01-en.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83bd/9744354/b7457d384826/0066-782X-abc-114-04-0718-gf02-en.jpg

相似文献

1
Artificial Intelligence in Cardiology: Concepts, Tools and Challenges - "The Horse is the One Who Runs, You Must Be the Jockey".人工智能在心脏病学中的应用:概念、工具和挑战 - “马是奔跑的那个,你必须是骑师”。
Arq Bras Cardiol. 2020 Apr;114(4):718-725. doi: 10.36660/abc.20180431. Epub 2020 May 29.
2
A systematic review on the impact of artificial intelligence on electrocardiograms in cardiology.关于人工智能对心脏病学中心电图影响的系统评价。
Int J Med Inform. 2025 Mar;195:105753. doi: 10.1016/j.ijmedinf.2024.105753. Epub 2024 Dec 9.
3
Collaborative AI and Laboratory Medicine integration in precision cardiovascular medicine.协同人工智能与实验室医学在精准心血管医学中的整合。
Clin Chim Acta. 2020 Oct;509:67-71. doi: 10.1016/j.cca.2020.06.001. Epub 2020 Jun 4.
4
Novel Artificial Intelligence Applications in Cardiology: Current Landscape, Limitations, and the Road to Real-World Applications.新型人工智能在心脏病学中的应用:现状、局限性及走向实际应用之路。
J Cardiovasc Transl Res. 2023 Jun;16(3):513-525. doi: 10.1007/s12265-022-10260-x. Epub 2022 Apr 22.
5
The application of artificial intelligence in nuclear cardiology.人工智能在核心脏病学中的应用。
Ann Nucl Med. 2022 Feb;36(2):111-122. doi: 10.1007/s12149-021-01708-2. Epub 2022 Jan 14.
6
The Emergence of Artificial Intelligence in Cardiology: Current and Future Applications.人工智能在心脏病学中的兴起:当前和未来的应用。
Curr Cardiol Rev. 2022;18(3):e191121198124. doi: 10.2174/1573403X17666211119102220.
7
Clinical applications of artificial intelligence in cardiology on the verge of the decade.人工智能在心脏病学中的临床应用即将进入十年。
Cardiol J. 2021;28(3):460-472. doi: 10.5603/CJ.a2020.0093. Epub 2020 Jul 10.
8
Artificial intelligence in pediatric cardiology: taking baby steps in the big world of data.儿科心脏病学中的人工智能:在大数据的广阔世界中迈出一小步。
Curr Opin Cardiol. 2022 Jan 1;37(1):130-136. doi: 10.1097/HCO.0000000000000927.
9
The premise, promise, and perils of artificial intelligence in critical care cardiology.人工智能在危重心血管病学中的前提、承诺和危险。
Prog Cardiovasc Dis. 2024 Sep-Oct;86:2-12. doi: 10.1016/j.pcad.2024.06.006. Epub 2024 Jun 25.
10
Artificial intelligence for breast cancer detection and its health technology assessment: A scoping review.用于乳腺癌检测的人工智能及其健康技术评估:一项范围综述。
Comput Biol Med. 2025 Jan;184:109391. doi: 10.1016/j.compbiomed.2024.109391. Epub 2024 Nov 22.

引用本文的文献

1
Application of the U-Net Deep Learning Model for Segmenting Single-Photon Emission Computed Tomography Myocardial Perfusion Images.U-Net深度学习模型在单光子发射计算机断层扫描心肌灌注图像分割中的应用。
Diagnostics (Basel). 2024 Dec 20;14(24):2865. doi: 10.3390/diagnostics14242865.
2
Deep learning from latent spatiotemporal information of the heart: Identifying advanced bioimaging markers from echocardiograms.从心脏潜在时空信息进行深度学习:从超声心动图中识别先进的生物成像标志物。
Biophys Rev (Melville). 2024 Mar 27;5(1):011304. doi: 10.1063/5.0176850. eCollection 2024 Mar.
3
Predicting major adverse cardiovascular events after orthotopic liver transplantation using a supervised machine learning model: A cohort study.

本文引用的文献

1
Comparison of Machine Learning Methods With National Cardiovascular Data Registry Models for Prediction of Risk of Bleeding After Percutaneous Coronary Intervention.机器学习方法与国家心血管数据注册模型预测经皮冠状动脉介入治疗后出血风险的比较。
JAMA Netw Open. 2019 Jul 3;2(7):e196835. doi: 10.1001/jamanetworkopen.2019.6835.
2
Extensive phenotype data and machine learning in prediction of mortality in acute coronary syndrome - the MADDEC study.急性冠状动脉综合征患者死亡率预测中的广泛表型数据和机器学习 - MADDEC 研究。
Ann Med. 2019 Mar;51(2):156-163. doi: 10.1080/07853890.2019.1596302. Epub 2019 Apr 27.
3
Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review.
使用监督式机器学习模型预测原位肝移植后的主要不良心血管事件:一项队列研究。
World J Hepatol. 2024 Feb 27;16(2):193-210. doi: 10.4254/wjh.v16.i2.193.
4
From beasts to bytes: Revolutionizing zoological research with artificial intelligence.从野兽到字节:人工智能引领动物学研究革命。
Zool Res. 2023 Nov 18;44(6):1115-1131. doi: 10.24272/j.issn.2095-8137.2023.263.
5
An online platform for COVID-19 diagnostic screening using a machine learning algorithm.基于机器学习算法的 COVID-19 诊断筛查在线平台。
Rev Assoc Med Bras (1992). 2023 Apr 14;69(4):e20221394. doi: 10.1590/1806-9282.20221394. eCollection 2023.
6
Machine learning-based predictive modeling of depression in hypertensive populations.基于机器学习的高血压人群抑郁预测模型。
PLoS One. 2022 Jul 29;17(7):e0272330. doi: 10.1371/journal.pone.0272330. eCollection 2022.
7
Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography.心脏肿瘤学中的人工智能机遇:聚焦心电图的概述
Am Heart J Plus. 2022 Mar;15. doi: 10.1016/j.ahjo.2022.100129. Epub 2022 Apr 1.
8
Artificial Algorithms Outperform Traditional Models in Predicting Coronary Artery Disease.在预测冠状动脉疾病方面,人工算法优于传统模型。
Arq Bras Cardiol. 2021 Dec;117(6):1071-1072. doi: 10.36660/abc.20210823.
9
Artificial intelligence and suicide prevention: a systematic review.人工智能与自杀预防:一项系统综述
Eur Psychiatry. 2022 Feb 15;65(1):1-22. doi: 10.1192/j.eurpsy.2022.8.
10
Initial application of deep learning to borescope detection of endoscope working channel damage and residue.深度学习在内窥镜工作通道损伤和残留物的管道镜检测中的初步应用。
Endosc Int Open. 2022 Jan 14;10(1):E112-E118. doi: 10.1055/a-1591-0258. eCollection 2022 Jan.
人工智能在心血管成像中的应用:JACC 最新综述
J Am Coll Cardiol. 2019 Mar 26;73(11):1317-1335. doi: 10.1016/j.jacc.2018.12.054.
4
Big data and black-box medical algorithms.大数据与黑盒医疗算法。
Sci Transl Med. 2018 Dec 12;10(471). doi: 10.1126/scitranslmed.aao5333.
5
From Evidence-Based Medicine to Precision Health: Using Data to Personalize Care.从循证医学到精准健康:利用数据实现个性化医疗。
Arq Bras Cardiol. 2018 Dec;111(6):762-763. doi: 10.5935/abc.20180240.
6
Fully Automated Echocardiogram Interpretation in Clinical Practice.临床实践中的全自动超声心动图解读。
Circulation. 2018 Oct 16;138(16):1623-1635. doi: 10.1161/CIRCULATIONAHA.118.034338.
7
Machine Meets Biology: a Primer on Artificial Intelligence in Cardiology and Cardiac Imaging.机器遇见生物学:人工智能在心脏病学和心脏成像中的应用入门。
Curr Cardiol Rep. 2018 Oct 18;20(12):139. doi: 10.1007/s11886-018-1074-8.
8
Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy.基于机器学习的心衰表型分组以识别心脏再同步治疗的反应者。
Eur J Heart Fail. 2019 Jan;21(1):74-85. doi: 10.1002/ejhf.1333. Epub 2018 Oct 17.
9
Prevalence of Burnout Among Physicians: A Systematic Review.医生职业倦怠的流行情况:一项系统性综述。
JAMA. 2018 Sep 18;320(11):1131-1150. doi: 10.1001/jama.2018.12777.
10
Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings.用于从短程单导联心电图记录中检测心房颤动的密集连接卷积网络。
J Electrocardiol. 2018 Nov-Dec;51(6S):S18-S21. doi: 10.1016/j.jelectrocard.2018.08.008. Epub 2018 Aug 10.