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

Ventilatory Modes01:14

Ventilatory Modes

141
Mechanical ventilators are life-saving devices that support or replace spontaneous breathing. They deliver breaths to patients through varying methods known as ventilator modes. Understanding these modes is critical for healthcare providers managing patients with respiratory failure.
There are three ventilatory modes: full support, partial support, and spontaneous. These are described below.
Full Support Modes
Full support modes include controlled mechanical ventilation, continuous mandatory...
141
Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

Assessment of Ventilation II: Respiratory Depth and Rhythm

1.5K
Respiratory Depth
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
To assess respiratory depth, observe the degree of chest excursion or movement:
1.5K
Mechanical Ventilation II: Invasive Ventilation01:23

Mechanical Ventilation II: Invasive Ventilation

132
Ventilators are essential medical equipment used to aid patients with respiratory difficulties. Their primary function is to assist or replace spontaneous breathing by providing mechanical ventilation. There are two general classes of mechanical ventilators: negative-pressure and positive-pressure ventilators.
Negative-Pressure Ventilators
Negative-pressure ventilators create a vacuum around the chest or body to draw air into the lungs, simulating breathing. This method does not require an...
132

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

From force to fate: Implications of mechanomemory in lung disease.

Respiratory research·2026
Same author

PEEP and alveolar recruitment after 60 years of acute respiratory distress syndrome.

Intensive care medicine·2026
Same author

Respiratory mechanics and patient-ventilator interaction dataset from the ICU.

Scientific data·2026
Same author

High-flow nasal cannula versus noninvasive ventilation in stabilized hypercapnic exacerbation: a physiological crossover trial.

Annals of intensive care·2026
Same author

Patient Support Following Post-Intensive Care Syndrome.

JAMA·2026
Same author

A precision approach to translational research in acute lung injury.

American journal of respiratory and critical care medicine·2026

相关实验视频

Updated: Jun 30, 2025

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis
05:56

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Published on: August 9, 2024

1.2K

通过监督深度学习技术检测到的平方流辅助通风过程中的流量饥饿.

Candelaria de Haro1,2, Verónica Santos-Pulpón3,4, Irene Telías5,6,7

  • 1Critical Care Department, Parc Taulí Hospital Universitari, Institut d'Investigació I Innovació Parc Taulí (I3PT-CERCA),, Carrer Parc Taulí, 1, 08208, Sabadell, Spain. cdeharo@tauli.cat.

Critical care (London, England)
|March 15, 2024
PubMed
概括

人工智能通过分析气道压力,准确地识别了流量饥饿,患者-通风器异步. 这种人工智能工具可以帮助实时最大限度地减少未被识别的不恰当的患者 - 呼吸机交互的情况.

关键词:
空气道的压力变形.人工智能算法的人工智能算法异步是不同步的流量饥饿是什么意思患者与呼吸器之间的相互作用

更多相关视频

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

490
Investigation into Deep Breathing through Measurement of Ventilatory Parameters and Observation of Breathing Patterns
08:34

Investigation into Deep Breathing through Measurement of Ventilatory Parameters and Observation of Breathing Patterns

Published on: September 16, 2019

11.6K

相关实验视频

Last Updated: Jun 30, 2025

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis
05:56

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Published on: August 9, 2024

1.2K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

490
Investigation into Deep Breathing through Measurement of Ventilatory Parameters and Observation of Breathing Patterns
08:34

Investigation into Deep Breathing through Measurement of Ventilatory Parameters and Observation of Breathing Patterns

Published on: September 16, 2019

11.6K

科学领域:

  • 关键护理医学 关键护理医学
  • 生物医学工程 生物医学工程
  • 医疗保健中的人工智能

背景情况:

  • 流量饥饿是一种患者-通风器异步,空气流无法满足需求,通常通过波形分析来确定.
  • 目前的临床诊断具有挑战性,容易导致诊断不足,这凸显了对自动检测的需求.
  • 人工智能为改善机械通风过程中气道压力变形的识别提供了机会.

研究的目的:

  • 开发一个监督的人工智能算法,用于检测气道压力变形.
  • 为了识别特定类型的患者-呼吸器异步在方流辅助通风和患者触发的呼吸.

主要方法:

  • 这是一项多中心的观察性研究,包括成人危急病患者在机械通风上24小时以上.
  • 五位重症监护专家将气道压力变形严重程度作为参考标准.
  • 卷积神经网络 (CNN) 和循环神经网络 (RNN) 模型被训练并对准确性,精度,回忆和F1分数进行评估.

主要成果:

  • 对6428次呼吸的分析显示,34%的呼吸道有严重的压力变形.
  • 在识别气道压力变形方面,RNN算法获得了87.9%的准确性,超过了CNN (86.8%).
  • 严重的变形与高的吸气力度有关 (ΔPes>>>10 cmH2O在74.4%的呼吸中).

结论:

  • 循环神经网络模型在识别气道压力变形时表现出色,这种变形是由流量饥饿引起的.
  • 这种人工智能工具有可能作为一个实时的,24小时的床边监控系统.
  • 实施这项技术可以帮助最大限度地减少未被识别的,不适当的患者 - 呼吸机交互的时期.