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

Asthma-IV: Diagnostic and Management01:30

Asthma-IV: Diagnostic and Management

The diagnosis and management of asthma are comprehensive, encompassing clinical assessments, lung function tests, and pharmacological interventions. Here's an overview:
Clinical Assessment for Asthma:
This is the first step in diagnosing and managing asthma. It includes:
Asthma-IV: Nursing Management01:30

Asthma-IV: Nursing Management

The nursing management of asthma is a comprehensive approach that relies heavily on the expertise and dedication of healthcare professionals. It involves thorough assessment, accurate diagnosis, strategic planning, effective implementation, and diligent evaluation. By meticulously following this step-by-step process, healthcare professionals play a crucial role in providing the best possible care and treatment for patients with asthma, enhancing their overall health and well-being.
First, in...
Asthma-I: Introduction01:29

Asthma-I: Introduction

Asthma is a chronic respiratory ailment that requires careful management due to its varying symptoms and influencing factors. It is characterized by airway inflammation, bronchial hyperresponsiveness, and reversible airflow obstruction, leading to symptoms like wheezing, shortness of breath, chest tightness, and coughing. The symptom frequency and intensity may vary considerably over time. It is also linked to immune system responses to allergens and irritants, highlighting the complex...
Asthma-II: Pathophysiology and Classification01:26

Asthma-II: Pathophysiology and Classification

Asthma is a prevalent chronic respiratory condition marked by inflammation and hyperresponsiveness of the airways. Its pathophysiology involves complex interactions among inflammatory pathways, immune responses, and neural mechanisms.
Additionally, environmental and genetic factors play crucial roles in determining an individual's susceptibility to asthma and the severity of their condition.
Critical processes in asthma pathophysiology include:
Chronic Obstructive Pulmonary Disease-IV: Assessement and Diagnostic Studies01:27

Chronic Obstructive Pulmonary Disease-IV: Assessement and Diagnostic Studies

Assessing and diagnosing Chronic Obstructive Pulmonary Disease (COPD) involves a detailed approach that includes a comprehensive review of medical history, physical examination, and a variety of diagnostic tests. This thorough evaluation is essential to ensure an accurate diagnosis and guide effective management strategies.
Medical History
Asthma: Pathogenesis and Management01:20

Asthma: Pathogenesis and Management

Asthma is a chronic pulmonary condition involving inflammation of the airways, hyper-reactivity, and reversible obstruction of the airways. This condition can significantly impact a person's quality of life, making breathing difficult and leading to distressing symptoms.
Asthma is classified as allergic and non-allergic. Allergens such as dust mites, pollen, and pet dander trigger allergic asthma, while factors like cold air, intense emotions, or exercise can induce non-allergic asthma.

You might also read

Related Articles

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

Sort by
Same author

Once-nightly oxybate dosing preference and nocturnal experience with twice-nightly oxybates: a plain language summary of publication.

Expert review of neurotherapeutics·2026
Same author

RESTORE: Once-nightly oxybate dosing preference and nocturnal experience with twice-nightly oxybates.

Sleep medicine: X·2024
Same author

Development and validation of a prognostic tool: Pulmonary embolism short-term clinical outcomes risk estimation (PE-SCORE).

PloS one·2021
Same author

Risk of intravenous amiodarone in patients with atrial fibrillation and ventricular preexcitation.

Pacing and clinical electrophysiology : PACE·2021
Same author

Effects of Solriamfetol on Quality-of-Life Measures from a 12-Week Phase 3 Randomized Controlled Trial.

Annals of the American Thoracic Society·2020
Same author

Nutrition education for cardiovascular disease prevention in individuals with spinal cord injuries: study protocol for a randomized controlled trial.

Trials·2017
Same journal

Clinical significance and effects of miR-431-5p in children with asthma.

The Journal of asthma : official journal of the Association for the Care of Asthma·2026
Same journal

Diagnostic Model Construction of Mitochondrial-Mitophagy Related Genes and Their Regulatory Network and Potential Drug Discovery in Childhood Allergic Asthma.

The Journal of asthma : official journal of the Association for the Care of Asthma·2026
Same journal

Sustained asthma control and remission in real-world patients with severe eosinophilic asthma receiving benralizumab: XALOC-2.

The Journal of asthma : official journal of the Association for the Care of Asthma·2026
Same journal

Exploring the Mechanism of Zhichuanling Oral Liquid in Treating Childhood Bronchial Asthma Based on Network Pharmacology and Experimental Verification.

The Journal of asthma : official journal of the Association for the Care of Asthma·2026
Same journal

Beyond BMI: A Three-Dimensional Integrative Framework of Morphology-Metabolism-Adipose Inflammation for Predicting Obesity-Related Asthma.

The Journal of asthma : official journal of the Association for the Care of Asthma·2026
Same journal

Comparison of three digital peak expiratory flow devices with spirometry in children and adolescents with asthma.

The Journal of asthma : official journal of the Association for the Care of Asthma·2026
See all related articles

Related Experiment Video

Updated: Jun 18, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Validating a guidelines-based asthma decision support system: step two.

Thomas Stern1, Jody Hunt, H James Norton

  • 1Department of Internal Medicine and the Department of Biostatistics of Carolinas Medical Center, PO Box 32861, Charlotte, NC 28232-2861, USA. Thomas.stern@carolinashealthcare.org

The Journal of Asthma : Official Journal of the Association for the Care of Asthma
|November 13, 2009
PubMed
Summary
This summary is machine-generated.

A computerized asthma decision support system achieved 100% accuracy in classifying asthma severity, outperforming pulmonologists. This advanced system offers superior diagnostic precision for asthma management.

Related Experiment Videos

Last Updated: Jun 18, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Area of Science:

  • Pulmonary Medicine
  • Medical Informatics
  • Clinical Decision Support

Background:

  • Previous work established a computerized asthma decision support system.
  • The system's accuracy in classifying asthma severity was previously measured against pulmonologists.
  • The potential for the system to exceed human expert performance was hypothesized.

Purpose of the Study:

  • To compare the accuracy of a computerized asthma decision support system against pulmonologists.
  • To evaluate performance using the 2007 Global Initiative for Asthma guidelines.
  • To determine which entity better classifies asthma severity.

Main Methods:

  • One hundred asthma case scenarios were developed and classified according to 2007 GINA guidelines, serving as the gold standard.
  • These scenarios were input into the computerized decision support system.
  • The same scenarios were presented to 10 practicing pulmonologists for classification.

Main Results:

  • The computerized decision support system demonstrated 100% sensitivity and specificity.
  • Pulmonologists achieved 94.2% sensitivity and 70.0% specificity.
  • Agreement with the gold standard was perfect for the system (Kappa = 1.00) versus substantial for pulmonologists (Kappa = 0.672).

Conclusions:

  • The computerized asthma decision support system significantly outperformed pulmonologists.
  • The system achieved superior accuracy in assigning asthma severity based on 2007 GINA guidelines.
  • This highlights the potential of AI in clinical asthma assessment.