• 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

Will Artificial Intelligence Replace the Human Echocardiographer?

作者信息

Sengupta Partho P, Adjeroh Donald A

机构信息

Division of Cardiology, West Virginia University Heart and Vascular Institute, School of Medicine (P.P.S.).

Lane Department of Computer Science and Electrical Engineering (D.A.A.), West Virginia University, Morgantown.

出版信息

Circulation. 2018 Oct 16;138(16):1639-1642. doi: 10.1161/CIRCULATIONAHA.118.037095.

DOI:10.1161/CIRCULATIONAHA.118.037095
PMID:30354473
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6448766/
Abstract
摘要

相似文献

1
Will Artificial Intelligence Replace the Human Echocardiographer?人工智能会取代人类超声心动图检查医师吗？
Circulation. 2018 Oct 16;138(16):1639-1642. doi: 10.1161/CIRCULATIONAHA.118.037095.
2
Applications of artificial intelligence and machine learning approaches in echocardiography.人工智能和机器学习方法在超声心动图中的应用。
Echocardiography. 2021 Jun;38(6):982-992. doi: 10.1111/echo.15048. Epub 2021 May 13.
3
Combining Artificial Intelligence With Human Insight to Automate Echocardiography.将人工智能与人类洞察力相结合以实现超声心动图自动化。
Circ Cardiovasc Imaging. 2019 Sep;12(9):e009727. doi: 10.1161/CIRCIMAGING.119.009727. Epub 2019 Sep 16.
4
Automation, machine learning, and artificial intelligence in echocardiography: A brave new world.超声心动图中的自动化、机器学习与人工智能：一个全新的世界。
Echocardiography. 2018 Sep;35(9):1402-1418. doi: 10.1111/echo.14086. Epub 2018 Jul 5.
5
Artificial intelligence: a new clinical support tool for stress echocardiography.人工智能：一种用于负荷超声心动图的新型临床支持工具。
Expert Rev Med Devices. 2018 Aug;15(8):513-515. doi: 10.1080/17434440.2018.1497482. Epub 2018 Jul 19.
6
Learning About Machine Learning to Create a Self-Driving Echocardiographic Laboratory.
Circulation. 2018 Oct 16;138(16):1636-1638. doi: 10.1161/CIRCULATIONAHA.118.037094.
7
How Bioethics Can Shape Artificial Intelligence and Machine Learning.生物伦理学如何塑造人工智能和机器学习
Hastings Cent Rep. 2018 Sep;48(5):10-13. doi: 10.1002/hast.895.
8
Trust Me, I'm a Chatbot: How Artificial Intelligence in Health Care Fails the Turing Test.相信我，我是个聊天机器人：医疗保健领域的人工智能如何无法通过图灵测试。
J Med Internet Res. 2019 Oct 28;21(10):e16222. doi: 10.2196/16222.
9
Artificial intelligence to support clinical decision-making processes.支持临床决策过程的人工智能。
EBioMedicine. 2019 Aug;46:27-29. doi: 10.1016/j.ebiom.2019.07.019. Epub 2019 Jul 11.
10
[Applications of machine learning in clinical decision support in the omic era].[机器学习在组学时代临床决策支持中的应用]
Yi Chuan. 2018 Sep 20;40(9):693-703. doi: 10.16288/j.yczz.18-139.

引用本文的文献

1
Editorial for the Special Issue "Novel Echocardiographic Techniques for the Assessment of Cardiovascular Disease".“用于评估心血管疾病的新型超声心动图技术”特刊社论
Medicina (Kaunas). 2025 Feb 15;61(2):345. doi: 10.3390/medicina61020345.
2
Patient Perspectives on AI-Driven Predictions of Schizophrenia Relapses: Understanding Concerns and Opportunities for Self-Care and Treatment.患者对人工智能驱动的精神分裂症复发预测的看法：了解自我护理和治疗中的问题与机遇
Proc SIGCHI Conf Hum Factor Comput Syst. 2024 May;2024. doi: 10.1145/3613904.3642369. Epub 2024 May 11.
3
Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review.

本文引用的文献

1
Fast and accurate view classification of echocardiograms using deep learning.使用深度学习对超声心动图进行快速准确的视图分类。
NPJ Digit Med. 2018;1. doi: 10.1038/s41746-017-0013-1. Epub 2018 Mar 21.
2
Fully Automated Echocardiogram Interpretation in Clinical Practice.临床实践中的全自动超声心动图解读。
Circulation. 2018 Oct 16;138(16):1623-1635. doi: 10.1161/CIRCULATIONAHA.118.034338.
3
Precision Phenotyping in Heart Failure and Pattern Clustering of Ultrasound Data for the Assessment of Diastolic Dysfunction.心力衰竭的精准表型分析和超声数据的模式聚类用于评估舒张功能障碍。
探讨人工智能技术在在线心理医疗保健中的作用：机遇、挑战及影响，一项混合方法综述
Front Psychiatry. 2024 May 7;15:1356773. doi: 10.3389/fpsyt.2024.1356773. eCollection 2024.
4
Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve.拥抱人工智能：超声心动图实验室领先潮流的必备工具。
Diagnostics (Basel). 2023 Oct 6;13(19):3137. doi: 10.3390/diagnostics13193137.
5
Role of Deep Learning in Computed Tomography.深度学习在计算机断层扫描中的作用。
Cureus. 2023 May 17;15(5):e39160. doi: 10.7759/cureus.39160. eCollection 2023 May.
6
Exploring the Potential of Artificial Intelligence in Pediatric Echocardiography-Preliminary Results from the First Pediatric Study Using AI Software Developed for Adults.探索人工智能在儿科超声心动图中的潜力——来自首次使用为成人开发的人工智能软件进行的儿科研究的初步结果。
J Clin Med. 2023 Apr 29;12(9):3209. doi: 10.3390/jcm12093209.
7
Artificial intelligence in cardiology: The past, present and future.心脏病学中的人工智能：过去、现在与未来。
Indian Heart J. 2022 Jul-Aug;74(4):265-269. doi: 10.1016/j.ihj.2022.07.004. Epub 2022 Jul 30.
8
Applications of Machine Learning in Cardiology.机器学习在心脏病学中的应用。
Cardiol Ther. 2022 Sep;11(3):355-368. doi: 10.1007/s40119-022-00273-7. Epub 2022 Jul 12.
9
Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging.用于临床决策的机器学习：心血管成像中的挑战与机遇
Front Cardiovasc Med. 2022 Jan 4;8:765693. doi: 10.3389/fcvm.2021.765693. eCollection 2021.
10
Cardiovascular Imaging and Intervention Through the Lens of Artificial Intelligence.透过人工智能视角看心血管成像与介入治疗
Interv Cardiol. 2021 Oct 20;16:e31. doi: 10.15420/icr.2020.04. eCollection 2021 Apr.
JACC Cardiovasc Imaging. 2017 Nov;10(11):1291-1303. doi: 10.1016/j.jcmg.2016.10.012. Epub 2017 Jan 18.
4
Burnout Among Cardiologists.心脏病专家的职业倦怠
Am J Cardiol. 2017 Mar 15;119(6):938-940. doi: 10.1016/j.amjcard.2016.11.052. Epub 2016 Dec 18.
5
Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography.机器学习算法在二维超声心动图中实现形态学和功能评估的自动化。
J Am Coll Cardiol. 2016 Nov 29;68(21):2287-2295. doi: 10.1016/j.jacc.2016.08.062.
6
The Supply and Demand of the Cardiovascular Workforce: Striking the Right Balance.心血管专业人员的供需：实现恰当平衡
J Am Coll Cardiol. 2016 Oct 11;68(15):1680-1689. doi: 10.1016/j.jacc.2016.06.070.
7
Characterization of myocardial motion patterns by unsupervised multiple kernel learning.基于无监督多内核学习的心肌运动模式特征描述。
Med Image Anal. 2017 Jan;35:70-82. doi: 10.1016/j.media.2016.06.007. Epub 2016 Jun 11.
8
Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy.用于心脏成像的认知机器学习算法：区分缩窄性心包炎与限制性心肌病的初步研究
Circ Cardiovasc Imaging. 2016 Jun;9(6). doi: 10.1161/CIRCIMAGING.115.004330.
9
Fully Automated Versus Standard Tracking of Left Ventricular Ejection Fraction and Longitudinal Strain: The FAST-EFs Multicenter Study.全自动与标准左心室射血分数和纵向应变追踪比较：FAST-EFs 多中心研究。
J Am Coll Cardiol. 2015 Sep 29;66(13):1456-66. doi: 10.1016/j.jacc.2015.07.052.
10
Identifying errors and inconsistencies in real time while using facilitated echocardiographic reporting.实时识别使用辅助超声心动图报告中的错误和不一致之处。
J Am Soc Echocardiogr. 2015 Jan;28(1):88-92.e1. doi: 10.1016/j.echo.2014.09.005. Epub 2014 Oct 16.