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相关概念视频

Physical Assessment of the Respiratory Tract IV: Auscultation01:28

Physical Assessment of the Respiratory Tract IV: Auscultation

446
Auscultation is a crucial component of the physical assessment of the respiratory tract. It offers valuable insights into airflow through the bronchial tree and potential lung obstructions. This process involves careful listening to breath, voice, and adventitious sounds, which can reveal a wealth of information about a patient's respiratory health.
Breath Sounds
Breath sounds are categorized into vesicular, bronchovesicular, and bronchial.
446
Assessment of Respiration01:23

Assessment of Respiration

1.2K
The respiratory system's basic structures and primary functions lay the foundation for nurses' comprehensive respiratory assessments. This assessment includes subjective and objective data to gauge the patient's respiratory health.
Subjective Assessment: Nurses interview the patient to gather information directly during the subjective assessment. It includes questions about the individual's medical history, medications, and symptoms, focusing on past respiratory conditions like...
1.2K
Heart Sounds01:15

Heart Sounds

2.0K
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)...
2.0K
Respiratory System Abnormal Finding II: Palpation and Auscultation01:31

Respiratory System Abnormal Finding II: Palpation and Auscultation

514
In assessing respiratory abnormalities, palpation and auscultation are critical tools for detecting and interpreting various pathophysiological changes. These techniques provide insight into underlying disorders by evaluating tactile sensations and sounds produced by the respiratory system.
Palpation Findings
During a respiratory assessment, palpation can reveal several vital abnormalities:
514
Physical Assessment of the Respiratory Tract III: Percussion01:29

Physical Assessment of the Respiratory Tract III: Percussion

490
The respiratory system, fundamental to life, consists of complex structures responsible for gas exchange. The percussion assessment is critical to understanding this system's health and functionality. This non-invasive assessment technique allows healthcare providers to evaluate the density or aeration of the lungs, thereby identifying potential abnormalities.
Percussion in Respiratory Assessment
Percussion evaluates underlying tissue composition with audible and tactile vibrations,...
490
Respiratory System Abnormal Finding I: Inspection and Percussion01:30

Respiratory System Abnormal Finding I: Inspection and Percussion

293
Respiratory system abnormalities are a significant concern in healthcare due to their potential to indicate underlying severe conditions like Chronic Obstructive Pulmonary Disease (COPD), asthma, and pneumonia. These abnormalities can often be detected through physical examination methods like inspection and percussion.
Inspection Findings
During an inspection, several findings may suggest the presence of respiratory distress or disease. Pursed-lip breathing, where exhalation is slowed by...
293

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相关实验视频

Updated: Jul 15, 2025

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

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基于深度学习的肺声分析用于智能耳语镜.

Dong-Min Huang1, Jia Huang2, Kun Qiao2

  • 1Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China.

Military Medical Research
|September 25, 2023
PubMed
概括

深度学习通过自动分析肺部声音来显著推进呼吸系统疾病的诊断. 这篇评论探讨了人工智能算法,数据集和智能听力镜的方法,解决当前的挑战并提供一个开源框架.

关键词:
深度学习是一种深度学习.肺部声音分析 肺部声音分析呼吸系统的声音.

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科学领域:

  • 医疗技术 医学技术
  • 人工智能的人工智能是人工智能.
  • 呼吸系统药物 呼吸系统药物

背景情况:

  • 传统的听觉有诸如主观性和无法录制声音等局限性.
  • 数字耳机可存储和共享声音,促进远程医疗和教育.
  • 机器学习,特别是深度学习,为智能诊断提供自动化肺声分析.

研究的目的:

  • 为肺部声音分析提供深度学习算法的全面概述.
  • 突出人工智能 (AI) 在推进呼吸道诊断中的作用.
  • 介绍一个开源框架,用于在这个领域标准化深度学习工作流程.

主要方法:

  • 对应用到肺部声音分析的深度学习算法的审查.
  • 专注于将肺部声音转换为2D谱图,用于卷积神经网络 (CNN) 分析.
  • 检查任务类别,公共数据集,除技术和最先进的方法.

主要成果:

  • 深度学习能够端到端识别呼吸系统疾病和异常的肺部声音.
  • 确定的挑战包括设备的可变性,噪声灵敏度和模型的可解释性.
  • 提供了一个开源框架,以提高可重现性和标准化.

结论:

  • 深度学习对智能耳机发展和自动呼吸道诊断具有重大潜力.
  • 应对当前的挑战对于广泛的临床采用至关重要.
  • 拟议的框架旨在促进合作,并推进人工智能驱动的肺声分析的研究.