Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Ventilatory Modes01:14

Ventilatory Modes

257
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...
257
Mechanical Ventilation III: Noninvasive Ventilation01:23

Mechanical Ventilation III: Noninvasive Ventilation

162
Noninvasive positive-pressure ventilation (NIPPV), continuous positive airway pressure (CPAP), and bilevel positive airway pressure (BiPAP) are essential methods in respiratory care. These ventilation techniques offer unique benefits for patients with various respiratory conditions, providing adequate support without requiring intubation. Let's explore how each method is crucial in improving patient outcomes and enhancing respiratory therapy.
Noninvasive Positive-Pressure Ventilation...
162
Mechanical Ventilation I: Indication and Settings01:29

Mechanical Ventilation I: Indication and Settings

569
Mechanical ventilation is a life-saving technique for managing acute respiratory failure and other respiratory complications. The process involves using a machine known as a ventilator to supply oxygen to the lungs and assist in removing carbon dioxide. It serves as a bridge to long-term mechanical ventilation or a temporary measure until ventilatory support is discontinued. The ventilator can maintain this function for a prolonged period, providing critical support for patients until they can...
569
Mechanical Ventilation II: Invasive Ventilation01:23

Mechanical Ventilation II: Invasive Ventilation

189
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...
189
Neural Control of Respiration01:18

Neural Control of Respiration

2.6K
The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...
2.6K
Cardiopulmonary Resuscitation II: ACLS Airway Management01:22

Cardiopulmonary Resuscitation II: ACLS Airway Management

26
Airway management is a key skill in emergency and critical care settings, as maintaining a clear airway is essential for adequate oxygenation and ventilation.Head Tilt-Chin Lift TechniqueThe head tilt-chin lift maneuver is an essential technique primarily used in patients without suspected cervical spine injuries. To perform this maneuver, one hand is placed on the patient’s forehead, and gentle pressure is applied backward to tilt the head. The fingertips of the other hand are positioned...
26

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Deep Learning-Enabled Robust Regional Lung Function Assessment in Mechanically Ventilated AECOPD Patients Using EIT Parameters.

Physiological measurement·2026
Same author

Engineering conformational transitions in silk fibroin hydrogels to create advanced dynamic microenvironments for biomedical applications.

Regenerative biomaterials·2026
Same author

Accurate delineation of cellular niches via integrated spatial transcriptomics and histological imaging with SYMOL.

Genome research·2026
Same author

Integrative transcriptome and microbiome analysis reveals ferroptosis-driven duodenal damage caused by Ochratoxin A in mice.

Frontiers in immunology·2026
Same author

Dynamic inflammatory trajectories and 28-day mortality in patients in the intensive care unit: An exploratory observational study.

Journal of intensive medicine·2026
Same author

Deep learning based automated assessment of end-inspiratory pause maneuver reliability in invasive mechanical ventilation.

Physiological measurement·2026

Related Experiment Video

Updated: Jul 31, 2025

Use of an Integrated Low-Flow Anesthetic Vaporizer, Ventilator, and Physiological Monitoring System for Rodents
06:57

Use of an Integrated Low-Flow Anesthetic Vaporizer, Ventilator, and Physiological Monitoring System for Rodents

Published on: July 9, 2020

6.0K

Reinforcement Learning Model for Managing Noninvasive Ventilation Switching Policy.

Xue Feng, Daoyuan Wang, Qing Pan

    IEEE Journal of Biomedical and Health Informatics
    |May 9, 2023
    PubMed
    Summary

    This study introduces an AI model using Double Dueling Deep Q Network to optimize noninvasive ventilation (NIV) treatment decisions for patients with COPD, aiming to reduce mortality and improve outcomes.

    More Related Videos

    Surfactant Depletion Combined with Injurious Ventilation Results in a Reproducible Model of the Acute Respiratory Distress Syndrome ARDS
    06:22

    Surfactant Depletion Combined with Injurious Ventilation Results in a Reproducible Model of the Acute Respiratory Distress Syndrome ARDS

    Published on: April 7, 2021

    3.5K
    Ex Vivo Porcine Experimental Model for Studying and Teaching Lung Mechanics
    12:09

    Ex Vivo Porcine Experimental Model for Studying and Teaching Lung Mechanics

    Published on: April 19, 2024

    1.5K

    Related Experiment Videos

    Last Updated: Jul 31, 2025

    Use of an Integrated Low-Flow Anesthetic Vaporizer, Ventilator, and Physiological Monitoring System for Rodents
    06:57

    Use of an Integrated Low-Flow Anesthetic Vaporizer, Ventilator, and Physiological Monitoring System for Rodents

    Published on: July 9, 2020

    6.0K
    Surfactant Depletion Combined with Injurious Ventilation Results in a Reproducible Model of the Acute Respiratory Distress Syndrome ARDS
    06:22

    Surfactant Depletion Combined with Injurious Ventilation Results in a Reproducible Model of the Acute Respiratory Distress Syndrome ARDS

    Published on: April 7, 2021

    3.5K
    Ex Vivo Porcine Experimental Model for Studying and Teaching Lung Mechanics
    12:09

    Ex Vivo Porcine Experimental Model for Studying and Teaching Lung Mechanics

    Published on: April 19, 2024

    1.5K

    Area of Science:

    • Artificial Intelligence in Medicine
    • Critical Care Medicine
    • Reinforcement Learning

    Background:

    • Noninvasive ventilation (NIV) is crucial for COPD and hypercapnic respiratory failure but optimal treatment switching strategies are unclear.
    • Delayed intubation or overtreatment during NIV can increase mortality and healthcare costs.
    • Personalized decision-making for NIV management is needed to improve patient outcomes.

    Purpose of the Study:

    • To develop an optimal regime model using offline-reinforcement learning for dynamic treatment decisions in NIV.
    • To reduce 28-day mortality in patients undergoing NIV by optimizing ventilation, discontinuation, or intubation strategies.
    • To evaluate the model's performance against physician strategies and its applicability across diverse patient subgroups.

    Main Methods:

    • Utilized Double Dueling Deep Q Network (D3QN), an offline-reinforcement learning algorithm, to create the treatment decision model.
    • Trained and tested the model using data from the Multi-Parameter Intelligent Monitoring in Intensive Care III (MIMIC-III) database.
    • Assessed the model's applicability across various disease subgroups categorized by the International Classification of Diseases (ICD).

    Main Results:

    • The D3QN model demonstrated a higher expected return score (4.25 vs. 2.68) compared to physician strategies.
    • Recommended treatments by the model reduced overall expected mortality from 27.82% to 25.44% in NIV patients.
    • For patients requiring intubation, the model suggested switching 13.36 hours earlier, reducing estimated mortality by 21.7%.

    Conclusions:

    • The developed AI model shows promise in dynamically personalizing NIV switching regimes for improved patient outcomes.
    • The model's ability to optimize treatment decisions across various respiratory disorders highlights its clinical potential.
    • This approach could significantly reduce mortality and healthcare costs associated with NIV management in critical care settings.