人工智能视角下的心血管成像与干预。

IF 0.2 0 PHILOSOPHY
Interventional Cardiology Review Pub Date : 2021-10-20 eCollection Date: 2021-04-01 DOI:10.15420/icr.2020.04
Karthik Seetharam, Sirish Shrestha, Partho P Sengupta
{"title":"人工智能视角下的心血管成像与干预。","authors":"Karthik Seetharam,&nbsp;Sirish Shrestha,&nbsp;Partho P Sengupta","doi":"10.15420/icr.2020.04","DOIUrl":null,"url":null,"abstract":"<p><p>Artificial Intelligence (AI) is the simulation of human intelligence in machines so they can perform various actions and execute decision-making. Machine learning (ML), a branch of AI, can analyse information from data and discover novel patterns. AI and ML are rapidly gaining prominence in healthcare as data become increasingly complex. These algorithms can enhance the role of cardiovascular imaging by automating many tasks or calculations, find new patterns or phenotypes in data and provide alternative diagnoses. In interventional cardiology, AI can assist in intraprocedural guidance, intravascular imaging and provide additional information to the operator. AI is slowly expanding its boundaries into interventional cardiology and can fundamentally alter the field. In this review, the authors discuss how AI can enhance the role of cardiovascular imaging and imaging in interventional cardiology.</p>","PeriodicalId":38586,"journal":{"name":"Interventional Cardiology Review","volume":"16 ","pages":"e31"},"PeriodicalIF":0.2000,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/94/b2/icr-16-e31.PMC8559149.pdf","citationCount":"9","resultStr":"{\"title\":\"Cardiovascular Imaging and Intervention Through the Lens of Artificial Intelligence.\",\"authors\":\"Karthik Seetharam,&nbsp;Sirish Shrestha,&nbsp;Partho P Sengupta\",\"doi\":\"10.15420/icr.2020.04\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Artificial Intelligence (AI) is the simulation of human intelligence in machines so they can perform various actions and execute decision-making. Machine learning (ML), a branch of AI, can analyse information from data and discover novel patterns. AI and ML are rapidly gaining prominence in healthcare as data become increasingly complex. These algorithms can enhance the role of cardiovascular imaging by automating many tasks or calculations, find new patterns or phenotypes in data and provide alternative diagnoses. In interventional cardiology, AI can assist in intraprocedural guidance, intravascular imaging and provide additional information to the operator. AI is slowly expanding its boundaries into interventional cardiology and can fundamentally alter the field. In this review, the authors discuss how AI can enhance the role of cardiovascular imaging and imaging in interventional cardiology.</p>\",\"PeriodicalId\":38586,\"journal\":{\"name\":\"Interventional Cardiology Review\",\"volume\":\"16 \",\"pages\":\"e31\"},\"PeriodicalIF\":0.2000,\"publicationDate\":\"2021-10-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/94/b2/icr-16-e31.PMC8559149.pdf\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Interventional Cardiology Review\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.15420/icr.2020.04\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2021/4/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"0\",\"JCRName\":\"PHILOSOPHY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Interventional Cardiology Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15420/icr.2020.04","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/4/1 0:00:00","PubModel":"eCollection","JCR":"0","JCRName":"PHILOSOPHY","Score":null,"Total":0}
引用次数: 9

摘要

人工智能(AI)是在机器中模拟人类智能,使它们能够执行各种动作并执行决策。机器学习(ML)是人工智能的一个分支,可以分析数据中的信息并发现新的模式。随着数据变得越来越复杂,人工智能和机器学习正在医疗保健领域迅速获得突出地位。这些算法可以通过自动化许多任务或计算来增强心血管成像的作用,在数据中发现新的模式或表型,并提供替代诊断。在介入心脏病学中,人工智能可以辅助术中指导、血管内成像,并为操作者提供额外的信息。人工智能正在慢慢将其边界扩展到介入心脏病学,并可能从根本上改变这一领域。在这篇综述中,作者讨论了人工智能如何增强心血管成像和影像学在介入心脏病学中的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Cardiovascular Imaging and Intervention Through the Lens of Artificial Intelligence.

Cardiovascular Imaging and Intervention Through the Lens of Artificial Intelligence.

Cardiovascular Imaging and Intervention Through the Lens of Artificial Intelligence.

Cardiovascular Imaging and Intervention Through the Lens of Artificial Intelligence.

Artificial Intelligence (AI) is the simulation of human intelligence in machines so they can perform various actions and execute decision-making. Machine learning (ML), a branch of AI, can analyse information from data and discover novel patterns. AI and ML are rapidly gaining prominence in healthcare as data become increasingly complex. These algorithms can enhance the role of cardiovascular imaging by automating many tasks or calculations, find new patterns or phenotypes in data and provide alternative diagnoses. In interventional cardiology, AI can assist in intraprocedural guidance, intravascular imaging and provide additional information to the operator. AI is slowly expanding its boundaries into interventional cardiology and can fundamentally alter the field. In this review, the authors discuss how AI can enhance the role of cardiovascular imaging and imaging in interventional cardiology.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Interventional Cardiology Review
Interventional Cardiology Review Medicine-Cardiology and Cardiovascular Medicine
CiteScore
0.30
自引率
0.00%
发文量
18
审稿时长
12 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信