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

Assessment of Ventilation I: Respiratory Rate01:20

Assessment of Ventilation I: Respiratory Rate

1.3K
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:
1.3K
Mechanical Ventilation II: Invasive Ventilation01:23

Mechanical Ventilation II: Invasive Ventilation

253
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...
253
Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

Assessment of Ventilation II: Respiratory Depth and Rhythm

1.8K
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:
1.8K
Mechanical Ventilation I: Indication and Settings01:29

Mechanical Ventilation I: Indication and Settings

932
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...
932
Mechanical Ventilation III: Noninvasive Ventilation01:23

Mechanical Ventilation III: Noninvasive Ventilation

224
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...
224
Ventilatory Modes01:14

Ventilatory Modes

431
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...
431

You might also read

Related Articles

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

Sort by
Same author

Reply to Liu et al.: Beyond Frequency: Rethinking Exacerbation Risk in COPD.

American journal of respiratory and critical care medicine·2026
Same author

Beyond spirometry in COPD: expanding the diagnostic paradigm.

ERJ open research·2026
Same author

Performance of multivariable risk prediction algorithms in predicting COPD exacerbations: a population-based study.

Thorax·2026
Same author

Summary of Research: Dupilumab for Chronic Obstructive Pulmonary Disease with Type 2 Inflammation: A Pooled Analysis of Two Phase 3, Randomised, Double-Blind, Placebo-Controlled Trials.

Pulmonary therapy·2026
Same author

The Utility of Advanced Imaging in COPD: Diagnosis, Prognosis, and Treatment-introductory Editorial.

The British journal of radiology·2026
Same author

Defining disease stability in COPD: Evidence from Phase 3 clinical trials.

American journal of respiratory and critical care medicine·2026
Same journal

Lung ultrasound in interstitial lung disease: from bedside screening to prognostic integration and AI-assisted standardization.

Respiratory medicine·2026
Same journal

Pathophysiology of silenthypoxia in COVID-19: Truth and mystery.

Respiratory medicine·2026
Same journal

Transformations within cystic fibrosis model of care in the era of CFTR modulators: a Scoping Review.

Respiratory medicine·2026
Same journal

Targeting the neutrophil-DPP-1-protease axis in airway disease: current evidence and future indications.

Respiratory medicine·2026
Same journal

The lung microbiome in hematopoietic stem cell transplantation: immune interactions, clinical consequences, and emerging interventions.

Respiratory medicine·2026
Same journal

GLP-1 receptor agonists in obstructive sleep apnea: A propensity score-matched real-world analysis.

Respiratory medicine·2026
See all related articles

Related Experiment Video

Updated: Sep 10, 2025

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

11.7K

Machine learning based detection of true ventilatory restriction.

Pratim Saha1, Muhammad F A Chaudhary2, Akm Shahariar Azad Rabby1

  • 1Center for Lung Analytics and Imaging Research, University of Alabama at Birmingham, Birmingham, AL, 35294, USA; Department of Computer Science, University of Alabama at Birmingham, AL, 35294, USA.

Respiratory Medicine
|August 21, 2025
PubMed
Summary
This summary is machine-generated.

A new machine learning model accurately detects true ventilatory restriction using spirometry and patient demographics, reducing the need for additional lung volume tests. This AI tool improves diagnostic accuracy for lung restriction.

Keywords:
Lung volumeMachine learningSpirometric restrictionVentilatory restriction

More Related Videos

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

3.4K
Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
09:42

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

706

Related Experiment Videos

Last Updated: Sep 10, 2025

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

11.7K
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

3.4K
Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
09:42

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

706

Area of Science:

  • Pulmonary Medicine
  • Machine Learning in Healthcare
  • Diagnostic Tools

Background:

  • Spirometry alone has limited accuracy (50%) in detecting true ventilatory restriction.
  • This necessitates supplementary lung volume tests for accurate diagnosis.
  • There is a need for improved methods to identify true ventilatory restriction.

Purpose of the Study:

  • To develop a novel detection tool for true lung restriction.
  • The tool integrates spirometry data with patient demographic information.
  • The aim is to enhance diagnostic accuracy and reduce reliance on lung volume tests.

Main Methods:

  • Analysis of spirometry and lung volume data from 21,062 participants.
  • Development of a LightGBM machine-learning model using spirometry (FEV1, FVC, FEV1/FVC, FEV1%pred, FVC%pred) and demographic (age, sex, BMI) features.
  • Model training and evaluation on distinct subsets of the cohort, with performance assessed via ROC analyses.

Main Results:

  • The developed LightGBM model achieved an accuracy of 0.78 (95% CI 0.77-0.80) and an AUC of 0.89 (95% CI 0.88-0.90).
  • The model demonstrated superior performance compared to spirometry alone, with sensitivity of 0.74 (95% CI 0.72-0.75) and specificity of 0.86 (95% CI 0.84-0.87).
  • Restrictive spirometric pattern alone had an accuracy of 0.61 for detecting true ventilatory restriction.

Conclusions:

  • A machine learning model incorporating spirometry and demographic data can effectively detect true ventilatory restriction.
  • This AI-driven approach significantly improves diagnostic accuracy over traditional spirometry alone.
  • The model shows potential to reduce the requirement for additional, more complex lung volume testing.