Machine Learning in Invasive and Noninvasive Coronary Angiography

IF 5.7 2区 医学 Q1 PERIPHERAL VASCULAR DISEASE
Ozan Unlu, Akl C. Fahed
{"title":"Machine Learning in Invasive and Noninvasive Coronary Angiography","authors":"Ozan Unlu, Akl C. Fahed","doi":"10.1007/s11883-023-01178-z","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose of Review</h3><p>The objective of this review is to shed light on the transformative potential of machine learning (ML) in coronary angiography. We aim to understand existing developments in using ML for coronary angiography and discuss broader implications for the future of coronary angiography and cardiovascular medicine.</p><h3 data-test=\"abstract-sub-heading\">Recent Findings</h3><p>The developments in invasive and noninvasive imaging have revolutionized diagnosis and treatment of coronary artery disease (CAD). However, CAD remains underdiagnosed and undertreated. ML has emerged as a powerful tool to further improve image analysis, hemodynamic assessment, lesion detection, and predictive modeling. These advancements have enabled more accurate identification of CAD, streamlined workflows, reduced the need for invasive diagnostic procedures, and improved the diagnostic value of invasive procedures when they are needed. Further integration of ML with coronary angiography will advance the prevention, diagnosis, and treatment of CAD.</p><h3 data-test=\"abstract-sub-heading\">Summary</h3><p>The integration of ML with coronary angiography is ushering in a new era in cardiovascular medicine. We highlight five use cases to leverage ML in coronary angiography: (1) improvement of quality and efficacy, (2) characterization of plaque, (3) hemodynamic assessment, (4) prediction of future outcomes, and (5) diagnosis of non-atherosclerotic coronary disease.</p>","PeriodicalId":10875,"journal":{"name":"Current Atherosclerosis Reports","volume":"3 1","pages":""},"PeriodicalIF":5.7000,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Atherosclerosis Reports","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s11883-023-01178-z","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PERIPHERAL VASCULAR DISEASE","Score":null,"Total":0}
引用次数: 0

Abstract

Purpose of Review

The objective of this review is to shed light on the transformative potential of machine learning (ML) in coronary angiography. We aim to understand existing developments in using ML for coronary angiography and discuss broader implications for the future of coronary angiography and cardiovascular medicine.

Recent Findings

The developments in invasive and noninvasive imaging have revolutionized diagnosis and treatment of coronary artery disease (CAD). However, CAD remains underdiagnosed and undertreated. ML has emerged as a powerful tool to further improve image analysis, hemodynamic assessment, lesion detection, and predictive modeling. These advancements have enabled more accurate identification of CAD, streamlined workflows, reduced the need for invasive diagnostic procedures, and improved the diagnostic value of invasive procedures when they are needed. Further integration of ML with coronary angiography will advance the prevention, diagnosis, and treatment of CAD.

Summary

The integration of ML with coronary angiography is ushering in a new era in cardiovascular medicine. We highlight five use cases to leverage ML in coronary angiography: (1) improvement of quality and efficacy, (2) characterization of plaque, (3) hemodynamic assessment, (4) prediction of future outcomes, and (5) diagnosis of non-atherosclerotic coronary disease.

Abstract Image

有创和无创冠状动脉造影中的机器学习
本综述的目的是阐明机器学习(ML)在冠状动脉造影中的变革潜力。我们的目标是了解使用ML进行冠状动脉造影的现有发展,并讨论冠状动脉造影和心血管医学的未来更广泛的意义。有创和无创影像技术的发展已经彻底改变了冠状动脉疾病(CAD)的诊断和治疗。然而,CAD仍未得到充分诊断和治疗。ML已成为进一步改进图像分析、血流动力学评估、病变检测和预测建模的强大工具。这些进步能够更准确地识别CAD,简化工作流程,减少对侵入性诊断程序的需求,并在需要时提高侵入性诊断程序的诊断价值。ML与冠状动脉造影的进一步结合将促进CAD的预防、诊断和治疗。ML与冠状动脉造影的结合正在引领心血管医学的新时代。我们强调了在冠状动脉造影中利用ML的五个应用案例:(1)提高质量和疗效,(2)斑块表征,(3)血流动力学评估,(4)预测未来结果,(5)非动脉粥样硬化性冠状动脉疾病的诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
9.00
自引率
3.40%
发文量
87
审稿时长
6-12 weeks
期刊介绍: The aim of this journal is to systematically provide expert views on current basic science and clinical advances in the field of atherosclerosis and highlight the most important developments likely to transform the field of cardiovascular prevention, diagnosis, and treatment. We accomplish this aim by appointing major authorities to serve as Section Editors who select leading experts from around the world to provide definitive reviews on key topics and papers published in the past year. We also provide supplementary reviews and commentaries from well-known figures in the field. An Editorial Board of internationally diverse members suggests topics of special interest to their country/region and ensures that topics are current and include emerging research.
×
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