• 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

Deep Learning for Assessment of Left Ventricular Ejection Fraction from Echocardiographic Images.

作者信息

Kusunose Kenya, Haga Akihiro, Yamaguchi Natsumi, Abe Takashi, Fukuda Daiju, Yamada Hirotsugu, Harada Masafumi, Sata Masataka

机构信息

Department of Cardiovascular Medicine, Tokushima University Hospital, Tokushima, Japan.

Department of Medical Image Informatics, Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan.

出版信息

J Am Soc Echocardiogr. 2020 May;33(5):632-635.e1. doi: 10.1016/j.echo.2020.01.009. Epub 2020 Feb 25.

DOI:10.1016/j.echo.2020.01.009
PMID:32111541
Abstract
摘要

相似文献

1
Deep Learning for Assessment of Left Ventricular Ejection Fraction from Echocardiographic Images.基于超声心动图图像的深度学习评估左心室射血分数
J Am Soc Echocardiogr. 2020 May;33(5):632-635.e1. doi: 10.1016/j.echo.2020.01.009. Epub 2020 Feb 25.
2
Deep learning-based automated left ventricular ejection fraction assessment using 2-D echocardiography.基于深度学习的二维超声心动图自动左心室射血分数评估。
Am J Physiol Heart Circ Physiol. 2021 Aug 1;321(2):H390-H399. doi: 10.1152/ajpheart.00416.2020. Epub 2021 Jun 25.
3
Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction.深度学习心电图算法检测左心室收缩功能障碍的前瞻性验证。
J Cardiovasc Electrophysiol. 2019 May;30(5):668-674. doi: 10.1111/jce.13889. Epub 2019 Mar 10.
4
Deep Learning-Based Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction: A Point-of-Care Solution.基于深度学习的左心室射血分数自动超声心动图定量分析：一种床旁解决方案。
Circ Cardiovasc Imaging. 2021 Jun;14(6):e012293. doi: 10.1161/CIRCIMAGING.120.012293. Epub 2021 Jun 15.
5
Evaluation of training nurses to perform semi-automated three-dimensional left ventricular ejection fraction using a customised workstation-based training protocol.使用基于定制工作站的培训方案对护士进行半自动化三维左心室射血分数测量操作培训的评估。
J Clin Nurs. 2015 Jun;24(11-12):1479-88. doi: 10.1111/jocn.12666. Epub 2014 Sep 25.
6
Echocardiographic determinants of left ventricular ejection fraction after acute myocardial infarction.急性心肌梗死后左心室射血分数的超声心动图决定因素
Am Heart J. 2000 Aug;140(2):284-9. doi: 10.1067/mhj.2000.107543.
7
Assessment of left ventricular function: comparison between radionuclide angiography and semiquantitative two-dimensional echocardiographic analysis.左心室功能评估：放射性核素血管造影与半定量二维超声心动图分析的比较
Eur J Nucl Med. 1996 Dec;23(12):1613-8. doi: 10.1007/BF01249624.
8
Identifying Phenogroups in patients with subclinical diastolic dysfunction using unsupervised statistical learning.使用无监督统计学习识别亚临床舒张功能障碍患者的表型组。
BMC Cardiovasc Disord. 2020 Aug 14;20(1):367. doi: 10.1186/s12872-020-01620-z.
9
Echocardiographic assessment of left ventricular systolic function.左心室收缩功能的超声心动图评估。
J Echocardiogr. 2019 Mar;17(1):10-16. doi: 10.1007/s12574-018-0405-5. Epub 2018 Nov 2.
10
Improved interobserver variability and accuracy of echocardiographic visual left ventricular ejection fraction assessment through a self-directed learning program using cardiac magnetic resonance images.通过使用心脏磁共振图像的自我指导学习计划，提高超声心动图视觉左心室射血分数评估的观察者间变异性和准确性。
J Am Soc Echocardiogr. 2013 Nov;26(11):1267-73. doi: 10.1016/j.echo.2013.07.017. Epub 2013 Aug 28.

引用本文的文献

1
Revolutionizing Cardiology: The Role of Artificial Intelligence in Echocardiography.心脏病学的变革：人工智能在超声心动图中的作用。
J Clin Med. 2025 Jan 19;14(2):625. doi: 10.3390/jcm14020625.
2
Feasibility validation of automatic diagnosis of mitral valve prolapse from multi-view echocardiographic sequences based on deep neural network.基于深度神经网络的多视图超声心动图序列自动诊断二尖瓣脱垂的可行性验证
Eur Heart J Imaging Methods Pract. 2024 Oct 28;2(4):qyae086. doi: 10.1093/ehjimp/qyae086. eCollection 2024 Oct.
3
Deep learning of echocardiography distinguishes between presence and absence of late gadolinium enhancement on cardiac magnetic resonance in patients with hypertrophic cardiomyopathy.
超声心动图的深度学习可区分肥厚型心肌病患者心脏磁共振成像中钆增强晚期的有无。
Echo Res Pract. 2024 Oct 14;11(1):23. doi: 10.1186/s44156-024-00059-8.
4
Diagnostic Modalities in Heart Failure: A Narrative Review.心力衰竭的诊断方法：叙述性综述
Cureus. 2024 Aug 21;16(8):e67432. doi: 10.7759/cureus.67432. eCollection 2024 Aug.
5
Transforming Echocardiography: The Role of Artificial Intelligence in Enhancing Diagnostic Accuracy and Accessibility.变革性超声心动图：人工智能在提高诊断准确性和可及性方面的作用。
Intern Med. 2025 Feb 1;64(3):331-336. doi: 10.2169/internalmedicine.4171-24. Epub 2024 Jul 25.
6
Artificial intelligence-enhanced automation of left ventricular diastolic assessment: a pilot study for feasibility, diagnostic validation, and outcome prediction.人工智能增强的左心室舒张功能评估自动化：一项关于可行性、诊断验证和结果预测的初步研究。
Cardiovasc Diagn Ther. 2024 Jun 30;14(3):352-366. doi: 10.21037/cdt-24-25. Epub 2024 Jun 17.
7
Development of automated neural network prediction for echocardiographic left ventricular ejection fraction.用于超声心动图左心室射血分数的自动化神经网络预测的开发。
Front Med (Lausanne). 2024 Apr 3;11:1354070. doi: 10.3389/fmed.2024.1354070. eCollection 2024.
8
A deep multi-stream model for robust prediction of left ventricular ejection fraction in 2D echocardiography.一种用于二维超声心动图中左心室射血分数稳健预测的深度多流模型。
Sci Rep. 2024 Jan 24;14(1):2104. doi: 10.1038/s41598-024-52480-y.
9
The Role of Artificial Intelligence in Echocardiography: A Clinical Update.人工智能在超声心动图中的作用：临床最新进展
Curr Cardiol Rep. 2023 Dec;25(12):1897-1907. doi: 10.1007/s11886-023-02005-2. Epub 2023 Dec 13.
10
Automatic echocardiographic evaluation of the probability of pulmonary hypertension using machine learning.使用机器学习对肺动脉高压可能性进行超声心动图自动评估。
Pulm Circ. 2023 Aug 4;13(3):e12272. doi: 10.1002/pul2.12272. eCollection 2023 Jul.