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相关概念视频

Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

602
Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin...
602
Electrocardiogram01:29

Electrocardiogram

2.4K
An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
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相关实验视频

Updated: Jul 9, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

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预测术后死亡率的电心图深度学习:一个模型开发和验证研究.

David Ouyang1, John Theurer2, Nathan R Stein2

  • 1Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

The Lancet. Digital health
|December 8, 2023
PubMed
概括
此摘要是机器生成的。

对心电图 (ECG) 的深度学习分析可以比传统方法更准确地预测术后死亡风险. 这种人工智能工具增强了各种医疗程序的风险分层,提高了患者的安全.

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科学领域:

  • 人工智能在医学中的应用
  • 心血管诊断心血管诊断服务
  • 预测分析在医疗保健中的应用

背景情况:

  • 目前的术前风险评估不足以确定患有术后死亡风险的患者.
  • 心电图 (ECG) 含有隐藏的风险标记,深度学习可以分析.
  • 需要改进的预后模型来预测术后死亡率.

研究的目的:

  • 开发和验证基于深度学习的预后模型,用于预测术后死亡率.
  • 评估模型的性能与修订的心脏风险指数 (RCRI) 相比.
  • 评估算法在不同医疗保健系统和手术类型中的通用性.

主要方法:

  • 开发了一个深度学习算法,使用来自衍生队列 (锡达斯西奈医疗中心) 的术前心电图.
  • 该算法利用心电图波形信号来区分术后死亡率.
  • 与RCRI相比,在内部和外部测试队列中,使用接收器运行特征曲线 (AUC) 下的面积来评估模型性能.

主要成果:

  • 深度学习算法在内部测试队列中实现了0.83的AUC,超过了RCRI得分 (AUC0.67).
  • 该算法在两个外部医疗保健系统 (AUC 0.79 和 0.75) 中表现强.
  • 通过算法识别的高风险患者与高RCRI得分 (2.08) 的患者相比,死亡率的几率比率明显更高 (8.83).

结论:

  • 术前心电图的深度学习解释显著改善了术后死亡风险的歧视.
  • 该算法在各种程序中有效,包括心脏手术,非心脏手术和导管实验室程序.
  • 这种人工智能工具可以为临床决策和患者风险分层提供宝贵的额外信息.