• 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

基于图像的机器学习心脏诊断：综述

Image-Based Cardiac Diagnosis With Machine Learning: A Review.

作者信息

Martin-Isla Carlos, Campello Victor M, Izquierdo Cristian, Raisi-Estabragh Zahra, Baeßler Bettina, Petersen Steffen E, Lekadir Karim

机构信息

Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain.

Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom.

出版信息

Front Cardiovasc Med. 2020 Jan 24;7:1. doi: 10.3389/fcvm.2020.00001. eCollection 2020.

DOI:10.3389/fcvm.2020.00001
PMID:32039241
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6992607/
Abstract

Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD). Until now, its role has been limited to visual and quantitative assessment of cardiac structure and function. However, with the advent of big data and machine learning, new opportunities are emerging to build artificial intelligence tools that will directly assist the clinician in the diagnosis of CVDs. This paper presents a thorough review of recent works in this field and provide the reader with a detailed presentation of the machine learning methods that can be further exploited to enable more automated, precise and early diagnosis of most CVDs.

摘要

心脏成像在心血管疾病（CVD）的诊断中起着重要作用。到目前为止，其作用仅限于对心脏结构和功能进行视觉和定量评估。然而，随着大数据和机器学习的出现，构建直接辅助临床医生诊断CVD的人工智能工具的新机会正在涌现。本文对该领域的近期研究进行了全面综述，并向读者详细介绍了机器学习方法，这些方法可进一步用于实现对大多数CVD更自动化、精确和早期的诊断。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec9/6992607/262d207749e4/fcvm-07-00001-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec9/6992607/01377f6cbd8f/fcvm-07-00001-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec9/6992607/1cf2f255900f/fcvm-07-00001-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec9/6992607/392a79f0d281/fcvm-07-00001-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec9/6992607/6710f856c4e1/fcvm-07-00001-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec9/6992607/1c3a19e8885f/fcvm-07-00001-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec9/6992607/01338da4d4e8/fcvm-07-00001-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec9/6992607/cddef7c82aee/fcvm-07-00001-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec9/6992607/262d207749e4/fcvm-07-00001-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec9/6992607/01377f6cbd8f/fcvm-07-00001-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec9/6992607/1cf2f255900f/fcvm-07-00001-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec9/6992607/392a79f0d281/fcvm-07-00001-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec9/6992607/6710f856c4e1/fcvm-07-00001-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec9/6992607/1c3a19e8885f/fcvm-07-00001-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec9/6992607/01338da4d4e8/fcvm-07-00001-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec9/6992607/cddef7c82aee/fcvm-07-00001-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec9/6992607/262d207749e4/fcvm-07-00001-g0008.jpg

相似文献

1
Image-Based Cardiac Diagnosis With Machine Learning: A Review.基于图像的机器学习心脏诊断：综述
Front Cardiovasc Med. 2020 Jan 24;7:1. doi: 10.3389/fcvm.2020.00001. eCollection 2020.
2
Clinical Application of Machine Learning-Based Artificial Intelligence in the Diagnosis, Prediction, and Classification of Cardiovascular Diseases.基于机器学习的人工智能在心血管疾病诊断、预测和分类中的临床应用
Circ J. 2021 Aug 25;85(9):1416-1425. doi: 10.1253/circj.CJ-20-1121. Epub 2021 Apr 22.
3
AI and the cardiologist: when mind, heart and machine unite.人工智能与心脏病学家：当思想、心脏与机器融为一体。
Open Heart. 2021 Dec;8(2). doi: 10.1136/openhrt-2021-001874.
4
Cardiac imaging: working towards fully-automated machine analysis & interpretation.心脏成像：迈向全自动机器分析与解读
Expert Rev Med Devices. 2017 Mar;14(3):197-212. doi: 10.1080/17434440.2017.1300057.
5
Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review.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.
6
Research Progress of Machine Learning and Deep Learning in Intelligent Diagnosis of the Coronary Atherosclerotic Heart Disease.机器学习和深度学习在冠状动脉粥样硬化性心脏病智能诊断中的研究进展。
Comput Math Methods Med. 2022 Apr 26;2022:3016532. doi: 10.1155/2022/3016532. eCollection 2022.
7
The Role of Artificial Intelligence in the Prediction, Diagnosis, and Management of Cardiovascular Diseases: A Narrative Review.人工智能在心血管疾病预测、诊断和管理中的作用：一项叙述性综述
Cureus. 2025 Mar 28;17(3):e81332. doi: 10.7759/cureus.81332. eCollection 2025 Mar.
8
Perfect Match: Radiomics and Artificial Intelligence in Cardiac Imaging.完美匹配：心脏影像中的放射组学和人工智能。
Circ Cardiovasc Imaging. 2024 Jun;17(6):e015490. doi: 10.1161/CIRCIMAGING.123.015490. Epub 2024 Jun 18.
9
Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine.人工智能、机器学习、计算机辅助诊断和放射组学：影像学向精准医学迈进的进展。
Radiol Bras. 2019 Nov-Dec;52(6):387-396. doi: 10.1590/0100-3984.2019.0049.
10
Radiomics in Echocardiography: Deep Learning and Echocardiographic Analysis.超声心动图中的放射组学：深度学习与超声心动图分析。
Curr Cardiol Rep. 2020 Jul 9;22(9):89. doi: 10.1007/s11886-020-01348-4.

引用本文的文献

1
Advances in artificial intelligence and precision nutrition approaches to improve maternal and child health in low resource settings.人工智能和精准营养方法在改善资源匮乏地区母婴健康方面的进展。
Nat Commun. 2025 Aug 18;16(1):7673. doi: 10.1038/s41467-025-62985-3.
2
CELM: An Ensemble Deep Learning Model for Early Cardiomegaly Diagnosis in Chest Radiography.CELM：一种用于胸部X光片中早期心脏肥大诊断的集成深度学习模型。
Diagnostics (Basel). 2025 Jun 25;15(13):1602. doi: 10.3390/diagnostics15131602.
3
Volatilome and machine learning in ischemic heart disease: Current challenges and future perspectives.

本文引用的文献

1
Machine learning in cardiovascular magnetic resonance: basic concepts and applications.机器学习在心血管磁共振中的应用：基础概念与应用
J Cardiovasc Magn Reson. 2019 Oct 7;21(1):61. doi: 10.1186/s12968-019-0575-y.
2
Cardiovascular magnetic resonance feature tracking strain analysis for discrimination between hypertensive heart disease and hypertrophic cardiomyopathy.心血管磁共振特征追踪应变分析在鉴别高血压性心脏病与肥厚型心肌病中的应用。
PLoS One. 2019 Aug 21;14(8):e0221061. doi: 10.1371/journal.pone.0221061. eCollection 2019.
3
Cardiac MRI and Texture Analysis of Myocardial T1 and T2 Maps in Myocarditis with Acute versus Chronic Symptoms of Heart Failure.
缺血性心脏病中的挥发物组学与机器学习：当前挑战与未来展望
World J Cardiol. 2025 Apr 26;17(4):106593. doi: 10.4330/wjc.v17.i4.106593.
4
Interoception, cardiac health, and heart failure: The potential for artificial intelligence (AI)-driven diagnosis and treatment.内感受、心脏健康与心力衰竭：人工智能驱动的诊断与治疗潜力
Physiol Rep. 2025 Jan;13(1):e70146. doi: 10.14814/phy2.70146.
5
Beyond genomic studies of congenital heart defects through systematic modelling and phenotyping.超越先天性心脏缺陷的基因组研究：通过系统建模和表型分析。
Dis Model Mech. 2024 Nov 1;17(11). doi: 10.1242/dmm.050913. Epub 2024 Nov 22.
6
A Short Review on the Impact of Artificial Intelligence in Diagnosis Diseases: Role of Radiomics In Neuro-Oncology.人工智能在疾病诊断中的影响综述：放射组学在神经肿瘤学中的作用
Galen Med J. 2023 Dec 16;12:e3158. doi: 10.31661/gmj.v12i.3158. eCollection 2023.
7
Applying a Deep Learning Model for Total Kidney Volume Measurement in Autosomal Dominant Polycystic Kidney Disease.应用深度学习模型测量常染色体显性多囊肾病中的总肾体积
Bioengineering (Basel). 2024 Sep 26;11(10):963. doi: 10.3390/bioengineering11100963.
8
Semi-supervised segmentation of cardiac chambers from LGE-CMR using feature consistency awareness.基于特征一致性感知的心脏 LGE-CMR 半监督分割。
BMC Cardiovasc Disord. 2024 Oct 17;24(1):571. doi: 10.1186/s12872-024-04250-x.
9
A Novel Approach to Identifying Hibernating Myocardium Using Radiomics-Based Machine Learning.一种基于放射组学的机器学习识别冬眠心肌的新方法。
Cureus. 2024 Sep 16;16(9):e69532. doi: 10.7759/cureus.69532. eCollection 2024 Sep.
10
The Role of Artificial Intelligence and Machine Learning in Cardiovascular Imaging and Diagnosis.人工智能和机器学习在心血管成像与诊断中的作用
Cureus. 2024 Sep 2;16(9):e68472. doi: 10.7759/cureus.68472. eCollection 2024 Sep.
心肌炎症伴急性与慢性心力衰竭症状的心脏 MRI 及心肌 T1 和 T2 图纹理分析。
Radiology. 2019 Sep;292(3):608-617. doi: 10.1148/radiol.2019190101. Epub 2019 Jul 30.
4
Using machine learning to predict one-year cardiovascular events in patients with severe dilated cardiomyopathy.利用机器学习预测重症扩张型心肌病患者的一年心血管事件。
Eur J Radiol. 2019 Aug;117:178-183. doi: 10.1016/j.ejrad.2019.06.004. Epub 2019 Jun 11.
5
Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease.深度超声心动图：面向心脏病自动诊断的高效数据监督式和半监督式深度学习
NPJ Digit Med. 2018 Oct 18;1:59. doi: 10.1038/s41746-018-0065-x. eCollection 2018.
6
DeepHarmony: A deep learning approach to contrast harmonization across scanner changes.DeepHarmony：一种深度学习方法，用于跨扫描仪变化进行对比调和。
Magn Reson Imaging. 2019 Dec;64:160-170. doi: 10.1016/j.mri.2019.05.041. Epub 2019 Jul 10.
7
Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow.基于表观流的半监督学习进行运动特征描述的电影 MRI 可解释性心脏病理学分类
Med Image Anal. 2019 Aug;56:80-95. doi: 10.1016/j.media.2019.06.001. Epub 2019 Jun 6.
8
Deep learning-enabled accurate normalization of reconstruction kernel effects on emphysema quantification in low-dose CT.深度学习可实现低剂量 CT 肺气肿定量中重建核效应的准确归一化。
Phys Med Biol. 2019 Jul 4;64(13):135010. doi: 10.1088/1361-6560/ab28a1.
9
A Deep Learning Approach for Assessment of Regional Wall Motion Abnormality From Echocardiographic Images.一种基于深度学习的超声心动图图像区域性壁运动异常评估方法。
JACC Cardiovasc Imaging. 2020 Feb;13(2 Pt 1):374-381. doi: 10.1016/j.jcmg.2019.02.024. Epub 2019 May 15.
10
Deep Learning for Diagnosis of Chronic Myocardial Infarction on Nonenhanced Cardiac Cine MRI.深度学习在非增强型心脏电影磁共振成像上诊断慢性心肌梗死的应用。
Radiology. 2019 Jun;291(3):606-617. doi: 10.1148/radiol.2019182304. Epub 2019 Apr 30.