Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Imaging Studies for Cardiovascular System I:Echocardiography01:17

Imaging Studies for Cardiovascular System I:Echocardiography

Cardiac imaging studies encompass a wide range of noninvasive and minimally invasive techniques designed to visualize the heart's structure and function in detail. One such technique is echocardiography, which uses high-frequency ultrasound waves to produce detailed images of the heart, known as echocardiograms.
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion, evaluates...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Clinical Patterns and Appropriateness of Apixaban Dosing in Patients With Atrial Fibrillation.

JACC. Advances·2026
Same author

Predictors of ischemic stroke and major bleeding among patients with atrial fibrillation in clinical practice.

American heart journal·2026
Same author

Self-supervised contrastive learning enables robust electrocardiogram-based cardiac classification.

Heart rhythm O2·2026
Same author

Uncertainty quantification of conduction velocity in models of cardiac spread of activation.

Medical & biological engineering & computing·2026
Same author

Hemodynamic Consequences and Clinical Outcomes With Intravenous Lidocaine Infusion in Patients With Atrial Fibrillation.

Journal of cardiovascular electrophysiology·2026
Same author

Institutional Factors and Shared Decision-Making for Atrial Fibrillation.

JAMA network open·2026

相关实验视频

Updated: Jun 7, 2026

Ultrasonic Assessment of Myocardial Microstructure
10:53

Ultrasonic Assessment of Myocardial Microstructure

Published on: January 14, 2014

5.4K

现成的机器学习架构的性能和偏差在左下心室喷射分数检测的左下心室喷射分数检测中的性能.

Jake A Bergquist1,2,3, Brian Zenger4, James Brundage4

  • 1Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah.

Heart rhythm O2
|November 4, 2024
PubMed
概括
此摘要是机器生成的。

现有人工智能机器学习 (AI-ML) 模型可以有效地从心电图 (ECG) 中检测左下心室喷射率 (LVEF). 然而,像种族和性别这样的患者特征可能会影响AI-ML预测的准确性,突出潜在的偏见.

关键词:
人工智能的人工智能是人工智能.电心电图 (ECG) 是一种心电图.可以解释的可解释性.心脏衰竭是因为心脏衰竭.机器学习 机器学习

更多相关视频

Murine Echocardiography of Left Atrium, Aorta, and Pulmonary Artery
08:17

Murine Echocardiography of Left Atrium, Aorta, and Pulmonary Artery

Published on: February 20, 2017

14.3K
Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
06:34

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography

Published on: October 28, 2020

3.9K

相关实验视频

Last Updated: Jun 7, 2026

Ultrasonic Assessment of Myocardial Microstructure
10:53

Ultrasonic Assessment of Myocardial Microstructure

Published on: January 14, 2014

5.4K
Murine Echocardiography of Left Atrium, Aorta, and Pulmonary Artery
08:17

Murine Echocardiography of Left Atrium, Aorta, and Pulmonary Artery

Published on: February 20, 2017

14.3K
Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
06:34

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography

Published on: October 28, 2020

3.9K

科学领域:

  • 心脏病学 心脏病学
  • 人工智能的人工智能
  • 机器学习 机器学习
  • 医疗信息学 医疗信息学

背景情况:

  • 人工智能机器学习 (AI-ML) 提供了从心电图 (ECG) 中提取临床见解的新方法.
  • 许多开源AI-ML架构存在,可适应各种应用.
  • 有限的研究已经探索了这些"现成"AI-ML模型在心电图分析中的实用性和局限性.

研究的目的:

  • 评估易于使用的AI-ML架构对ECG分析的有效性.
  • 为了确定哪些"现成"AI-ML模型适合ECG解释.
  • 了解基于心电图的LVEF检测中的AI-ML方法的故障模式和潜在偏差.

主要方法:

  • 应用了6个"现成"的AI-ML架构,对24868个ECG的大量队列进行了应用.
  • 评估了这些模型在检测左下心室喷射率 (LVEF) 中的性能.
  • 研究的患者特征与不准确的LVEF预测 (假阳性/假阴性) 相关.

主要成果:

  • 所有测试的架构都实现了接收器运行特征曲线 (AUC) 下的面积在LVEF检测上高于0.9.
  • ResNet 18架构表现出最高的性能,平均AUC为0.917.
  • 包括种族,性别和并发症在内的患者因素与LVEF预测准确度的降低有关.

结论:

  • "现成"AI-ML架构可以实现与ECG分析定制模型可比的性能.
  • 该研究确定了AI-ML ECG解释中与患者特征相关的潜在偏差.
  • 调查结果强调需要仔细考虑在医疗保健中部署AI-ML的效率和公平性.