文献检索文档翻译深度研究
Suppr Zotero 插件Zotero 插件
邀请有礼套餐&价格历史记录

新学期,新优惠

限时优惠:9月1日-9月22日

30天高级会员仅需29元

1天体验卡首发特惠仅需5.99元

了解详情
不再提醒
插件&应用
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
高级版
套餐订阅购买积分包
AI 工具
文献检索文档翻译深度研究
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 @ 2025

Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist.

作者信息

Siegersma K R, Leiner T, Chew D P, Appelman Y, Hofstra L, Verjans J W

机构信息

Department of Cardiology, location VUmc, Amsterdam University Medical Centres, Amsterdam, The Netherlands.

Department of Experimental Cardiology, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.

出版信息

Neth Heart J. 2019 Sep;27(9):403-413. doi: 10.1007/s12471-019-01311-1.


DOI:10.1007/s12471-019-01311-1
PMID:31399886
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6712136/
Abstract

Healthcare, conceivably more than any other area of human endeavour, has the greatest potential to be affected by artificial intelligence (AI). This potential has been shown by several reports that demonstrate equal or superhuman performance in medical tasks that aim to improve efficiency, diagnosis and prognosis. This review focuses on the state of the art of AI applications in cardiovascular imaging. It provides an overview of the current applications and studies performed, including the potential value, implications, limitations and future directions of AI in cardiovascular imaging.It is envisioned that AI will dramatically change the way doctors practise medicine. In the short term, it will assist physicians with easy tasks, such as automating measurements, making predictions based on big data, and putting clinical findings into an evidence-based context. In the long term, AI will not only assist doctors, it has the potential to significantly improve access to health and well-being data for patients and their caretakers. This empowers patients. From a physician's perspective, reliable AI assistance will be available to support clinical decision-making. Although cardiovascular studies implementing AI are increasing in number, the applications have only just started to penetrate contemporary clinical care.

摘要

相似文献

[1]
Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist.

Neth Heart J. 2019-9

[2]
Applications of artificial intelligence in multimodality cardiovascular imaging: A state-of-the-art review.

Prog Cardiovasc Dis. 2020-3-19

[3]
Artificial intelligence (AI) and interventional radiotherapy (brachytherapy): state of art and future perspectives.

J Contemp Brachytherapy. 2020-10

[4]
Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease.

Cardiovasc Res. 2020-11-1

[5]
Impact of Artificial Intelligence on Interventional Cardiology: From Decision-Making Aid to Advanced Interventional Procedure Assistance.

JACC Cardiovasc Interv. 2019-7-22

[6]
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.

Diagnostics (Basel). 2024-5-26

[7]
Artificial intelligence in reproductive medicine.

Reproduction. 2019-10

[8]
Artificial Intelligence Will Transform Cardiac Imaging-Opportunities and Challenges.

Front Cardiovasc Med. 2019-9-10

[9]
Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review.

J Am Coll Cardiol. 2019-3-26

[10]
Needs, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum.

JMIR Med Educ. 2022-6-7

引用本文的文献

[1]
The Role of Artificial Intelligence in Heart Failure Diagnostics, Risk Prediction, and Therapeutic Strategies: A Comprehensive Review.

Cureus. 2025-7-1

[2]
Insights Into the Future: Assessing Medical Students' Artificial Intelligence Readiness - A Cross-Sectional Study at Kerman University of Medical Sciences (2022).

Health Sci Rep. 2025-5-26

[3]
Bibliometric analysis of artificial intelligence applications in cardiovascular imaging: trends, impact, and emerging research areas.

Ann Med Surg (Lond). 2025-2-28

[4]
Factors affecting medical artificial intelligence (AI) readiness among medical students: taking stock and looking forward.

BMC Med Educ. 2025-2-18

[5]
The Role of Artificial Intelligence and Machine Learning in Cardiovascular Imaging and Diagnosis.

Cureus. 2024-9-2

[6]
Computer-aided analysis of radiological images for cancer diagnosis: performance analysis on benchmark datasets, challenges, and directions.

EJNMMI Rep. 2024-4-1

[7]
Determining medical students' anxiety and readiness levels about artificial intelligence.

Heliyon. 2024-2-9

[8]
Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes.

Diagnostics (Basel). 2024-1-10

[9]
Healthcare students' knowledge, attitudes, and perspectives toward artificial intelligence in the southern Vietnam.

Heliyon. 2023-11-22

[10]
The Nottingham Ischaemic Cardiovascular Magnetic Resonance resource (NotIs CMR): a prospective paired clinical and imaging scar database-protocol.

J Cardiovasc Magn Reson. 2023-11-27

本文引用的文献

[1]
Fast and accurate view classification of echocardiograms using deep learning.

NPJ Digit Med. 2018

[2]
Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification.

J Cardiovasc Magn Reson. 2019-1-7

[3]
A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography.

IEEE Trans Med Imaging. 2018-11-28

[4]
Fully Automated Echocardiogram Interpretation in Clinical Practice.

Circulation. 2018-10-16

[5]
Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy.

Eur J Heart Fail. 2018-10-17

[6]
Extraction of Ejection Fraction from Echocardiography Notes for Constructing a Cohort of Patients having Heart Failure with reduced Ejection Fraction (HFrEF).

J Med Syst. 2018-9-25

[7]
Automated cardiovascular magnetic resonance image analysis with fully convolutional networks.

J Cardiovasc Magn Reson. 2018-9-14

[8]
Deep Learning-A Technology With the Potential to Transform Health Care.

JAMA. 2018-9-18

[9]
Diagnosis of Heart Failure With Preserved Ejection Fraction: Machine Learning of Spatiotemporal Variations in Left Ventricular Deformation.

J Am Soc Echocardiogr. 2018-8-23

[10]
3-D Consistent and Robust Segmentation of Cardiac Images by Deep Learning With Spatial Propagation.

IEEE Trans Med Imaging. 2018-3-29

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

推荐工具

医学文档翻译智能文献检索