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相关概念视频

Electrocardiogram01:29

Electrocardiogram

2.3K
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...
2.3K
Pulse rhythm01:30

Pulse rhythm

797
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
797

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相关实验视频

Updated: Jul 1, 2025

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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人工智能启用心电图学有助于甲状腺功能障碍的检测和预测结果.

Chin Lin1,2, Feng-Chih Kuo3, Tom Chau4

  • 1School of Medicine, National Defense Medical Center, Taipei, Taiwan ROC.

Communications medicine
|March 13, 2024
PubMed
概括

人工智能模型可以使用心电图来检测甲状腺功能障碍,识别患有较高死亡和心力衰竭风险的患者. 这种人工智能工具有助于早期评估心血管疾病风险.

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相关实验视频

Last Updated: Jul 1, 2025

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科学领域:

  • 心脏病学 心脏病学
  • 内分泌学 在内分泌学.
  • 人工智能在医学中的应用

背景情况:

  • 甲状腺功能障碍经常被诊断不足,导致心力衰竭和死亡率的重大风险.
  • 早期识别高风险个体对于有效的抗甲状腺治疗至关重要.
  • 电心电图 (ECG) 可以揭示与甲状腺功能障碍相关的心脏电气变化.

研究的目的:

  • 开发和验证人工智能 (AI) 模型,通过心电图检测甲状腺功能障碍.
  • 评估人工智能模型在预测心血管结果方面的能力,包括死亡率和心力衰竭.

主要方法:

  • 一个深度学习模型在33,246名患者的47,245个心电图上接受了训练,这些患者有可用的甲状腺刺激激素 (TSH) 测量.
  • 使用TSH和自由甲状腺素水平来定义过度和亚临床甲状腺功能障碍.
  • 该模型经过内部验证 (14,420名患者) 和外部验证 (11,498名和596名患者).

主要成果:

  • 人工智能模型在检测甲状腺功能障碍 (AUC 0.725-0.761) 和明显甲状腺功能障碍 (AUC 0.867-0.876) 中表现出强的表现.
  • 亚临床甲状腺功能障碍检测的性能为AUC 0.631-0.701,超过了传统的机器学习模型.
  • 人工智能识别的甲状腺功能障碍患者面临1.97-2.94倍的死亡和新发性心力衰竭风险,特别是那些患有亚临床甲状腺功能障碍患者.

结论:

  • 一个人工智能驱动的算法有效地使用心电图数据识别明显的和亚临床的甲状腺功能障碍.
  • 该算法增强了心血管风险分层,特别是在亚临床甲状腺功能障碍症患者中.
  • 这种人工智能方法为甲状腺功能障碍的早期检测和风险评估提供了一个新的工具.