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相关概念视频

Heart Sounds01:15

Heart Sounds

1.9K
Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
S1, also known as the "lub" sound, is caused by the closure of atrioventricular (A-V)...
1.9K
Cardiovascular System Abnormal Findings II: Auscultation01:25

Cardiovascular System Abnormal Findings II: Auscultation

141
Auscultation, an essential part of a heart examination, is done using a stethoscope. It provides crucial information about heart function and possible heart problems. Due to heart problems, abnormal sounds can be heard during systole or diastole. These sounds include S3 and S4 gallops, opening snaps, systolic clicks, and murmurs.
Abnormal Heart Sounds
Gallops:
141
Assessment of the Cardiovascular System IV: Auscultation01:25

Assessment of the Cardiovascular System IV: Auscultation

322
Cardiac auscultation is a clinical skill used to assess heart function and detect abnormalities. It involves listening to heart sounds at specific anatomical locations through a stethoscope.
Normal Heart Sounds
S1 (First Heart Sound)-
S1 is made by the closure of the mitral and tricuspid valves (atrioventricular valves), marking the beginning of systole.
S2 (Second Heart Sound)-
S2 is made by the closure of the aortic and pulmonic valves (semilunar valves), marking the end of the systole.
322
Heart Valves01:16

Heart Valves

4.6K
The human heart is a complex organ with an intricate system of valves that regulate blood flow. There are two main types of valves: atrioventricular (AV) valves and semilunar valves.
The AV valves prevent the backflow of blood from the ventricles to the atria during ventricular contraction. These valves function with the assistance of the chordae tendineae and papillary muscles. When the ventricles are relaxed, the chordae tendineae are slack, allowing blood to flow from the atria into the...
4.6K

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相关实验视频

Updated: Jul 1, 2025

Semi-automated Optical Heartbeat Analysis of Small Hearts
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Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

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使用心脏声音和深度学习算法的自动膜心脏病检测.

Zihan Jiang1, Wenhua Song2, Yonghong Yan3

  • 1Arrhythmia Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.

International journal of cardiology. Heart & vasculature
|March 14, 2024
PubMed
概括

使用深度学习的人工智能可以根据心脏声音准确诊断膜心脏病 (VHD). 这种人工智能工具有助于VHD查,诊断和随访,解决临床听觉方面的局限性.

关键词:
心脏的声音,心脏的声音.机器学习是机器学习.神经网络的神经网络的神经网络身体检查 身体检查膜心脏病是一种心脏病.

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Ultrasonic Assessment of Myocardial Microstructure
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Ultrasonic Assessment of Myocardial Microstructure

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Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
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Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

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相关实验视频

Last Updated: Jul 1, 2025

Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

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Ultrasonic Assessment of Myocardial Microstructure
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Ultrasonic Assessment of Myocardial Microstructure

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Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
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科学领域:

  • 心脏病学 心脏病学
  • 人工智能的人工智能
  • 医学诊断 医学诊断 医学诊断

背景情况:

  • 临床医生的临床听觉在诊断膜心脏病 (VHD) 中存在局限性.
  • 人工智能 (AI) 提供了一个潜在的解决方案,通过分析心脏声音来增强VHD诊断.
  • 人工智能在自动诊断VHD方面的有效性需要进一步研究.

研究的目的:

  • 开发和评估一种深度学习模型,用于识别需要使用原始心脏声音数据进行干预的VHD患者.
  • 将AI模型的诊断性能与已建立的临床标准进行比较.

主要方法:

  • 收集了VHD患者和健康对照者的心脏声音数据,使用电子耳语镜.
  • 利用心声回声作为VHD诊断的黄金标准.
  • 在早期注册的数据上训练了一种深度学习模型,并在晚期注册的数据上验证它.

主要成果:

  • 这项研究包括499名患者 (354名接受培训,145名接受验证).
  • 深度学习模型在识别各种VHD (71.4-100.0%) 中表现出高灵敏度,特异性和准确性.
  • 关节狭窄显示出最好的诊断性能,具有100%的灵敏度,特异性和准确性.

结论:

  • 深度学习模型可以有效地从原始心脏声音数据中识别VHD患者.
  • 人工智能辅助的VHD诊断显示了改善查,诊断和后续过程的希望.
  • 这项技术有可能弥补人类听觉技能的局限性.