Jove
Visualize
Contact Us

Related Concept Videos

Mechanical Ventilation I: Indication and Settings01:29

Mechanical Ventilation I: Indication and Settings

4.2K
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...
4.2K
Ventilatory Modes01:14

Ventilatory Modes

2.3K
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...
2.3K
Mechanical Ventilation II: Invasive Ventilation01:23

Mechanical Ventilation II: Invasive Ventilation

1.3K
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...
1.3K
Mechanical Ventilation III: Noninvasive Ventilation01:23

Mechanical Ventilation III: Noninvasive Ventilation

931
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...
931
Assessment of Ventilation I: Respiratory Rate01:20

Assessment of Ventilation I: Respiratory Rate

2.8K
Assessment of Ventilation
A Ventilation assessment is critical for monitoring a patient's health status. Respiration, one of the most accessible vital signs, provides insights into the function of numerous body systems and can indicate serious health issues, such as brainstem injuries from head trauma.
Critical Guidelines for Assessing Ventilation:
2.8K
Factors Affecting Pulmonary Ventilation01:19

Factors Affecting Pulmonary Ventilation

3.2K
Besides the pressure difference between the external environment and the lungs, the airflow rate and ease of pulmonary ventilation are also influenced by three other factors: surface tension of the fluid in the alveoli, compliance of the lungs, and airway resistance.
Alveolar Surface Tension
The alveolar fluid lines the luminal surface of the alveoli and exerts a force called surface tension. This force is caused by the polar water molecules in the liquid being more strongly attracted to each...
3.2K

You might also read

Related Articles

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

Sort by
Same author

Deep Model Families for EEG-Based Multi-Class Dementia Classification.

International journal of neural systems·2026
Same author

On the Use of a Depth Camera for the Assessment of Upper Extremity Movements in Healthy Individuals.

Sensors (Basel, Switzerland)·2026
Same author

CoSuBio: Confidence and success dataset based on multimodal biosignals.

Data in brief·2026
Same author

Multimodal insights into diverse pain experiences: PhysioPain dataset.

Data in brief·2025
Same author

Biosignals, facial expressions, and speech as measures of workplace stress: Workstress3d dataset.

Data in brief·2024
Same author

NeuroBioSense: A multidimensional dataset for neuromarketing analysis.

Data in brief·2024
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 Experiment Video

Updated: May 4, 2026

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

5.7K

A decision support system to determine optimal ventilator settings.

Fatma Patlar Akbulut1, Erkan Akkur, Aydin Akan

  • 1Department of Computer Engineering, Istanbul Kültür University, Istanbul, Turkey. f.patlar@iku.edu.tr.

BMC Medical Informatics and Decision Making
|January 14, 2014
PubMed
Summary

This study introduces an AI-powered decision support system to optimize ventilator settings for respiratory patients, aiming to reduce errors and improve patient outcomes in critical care. The system achieved 98.44% accuracy, assisting less experienced physicians.

More Related Videos

Mechanical Ventilation Boot Camp Curriculum
07:36

Mechanical Ventilation Boot Camp Curriculum

Published on: March 12, 2018

9.7K
Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit
05:56

Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit

Published on: September 6, 2024

7.4K

Related Experiment Videos

Last Updated: May 4, 2026

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

5.7K
Mechanical Ventilation Boot Camp Curriculum
07:36

Mechanical Ventilation Boot Camp Curriculum

Published on: March 12, 2018

9.7K
Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit
05:56

Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit

Published on: September 6, 2024

7.4K

Area of Science:

  • Medical Technology
  • Artificial Intelligence in Medicine
  • Respiratory Care

Background:

  • Selecting appropriate ventilator settings is crucial for treating respiratory tract diseases, with physician experience directly impacting patient outcomes.
  • Inaccurate ventilator configuration can lead to critical errors and preventable deaths.
  • Decision support systems (DSS) are increasingly utilized to minimize errors in medical procedures.

Purpose of the Study:

  • To develop and evaluate a DSS for recommending optimal ventilator settings based on patient physiological data.
  • To reduce errors in mechanical ventilation therapy and support less experienced clinicians.
  • To enhance efficiency in clinical decision-making for ventilator management.

Main Methods:

  • An Artificial Neural Network (ANN) model was developed to calculate key ventilator parameters (frequency, tidal volume, FiO2) and estimate support levels (pressure/volume support).
  • The ANN model underwent various configuration tests to optimize performance.
  • Training methods and the number of hidden layers were evaluated for their impact on ANN performance.

Main Results:

  • The system was trained and tested using physiological data from 158 respiratory patients over 60.
  • The DSS recommended ventilator settings with 98.44% accuracy, considering parameters like PEEP, pH, and blood pressure.
  • Specific ANN training algorithms (sequential order weight/bias for regression, Bayesian regulation backpropagation for classification) demonstrated optimal performance.

Conclusions:

  • The proposed DSS aims to standardize ventilator parameter selection, reducing reliance on individual physician expertise.
  • Increased patient data in training significantly enhances the system's predictive accuracy.
  • The system can empower non-physician operators to manage ventilator settings effectively, especially in emergency situations.