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-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 I: Introduction01:28

Asthma I: Introduction

Asthma is a chronic inflammatory disorder of the airways characterized by variable airflow obstruction and heightened bronchial responsiveness to a wide range of triggers. The underlying inflammation leads to airway swelling, mucus hypersecretion, and smooth muscle constriction, all of which narrow the airway lumen and impede airflow. Clinically, asthma presents with recurrent episodes of wheezing, shortness of breath, chest tightness, and coughing, symptoms that typically vary in intensity and...
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:
Asthma-III: Symptoms and Complications01:24

Asthma-III: Symptoms and Complications

Asthma, a common chronic respiratory condition, is classified considering the frequency and severity of symptoms alongside lung function impairment. Understanding this classification is essential for appropriate treatment and management. Here's a detailed look at the classification of asthma and its clinical features and complications:
Classification of Asthma
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...

You might also read

Related Articles

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

Sort by
Same author

Clinical Manifestations and Genetic Findings in Three Patients with Chediak-Higashi Syndrome: Highlighting the Splice Site Variants.

Iranian journal of allergy, asthma, and immunology·2026
Same author

Nurses' Experiences of Discriminatory Care Towards Vulnerable Patients: A Qualitative Study From Iran.

International journal of older people nursing·2026
Same author

Recruitment and Participation of Black Home Health Care Patients in Speech-Based Cognitive Research: Mixed Methods Feasibility Study.

JMIR formative research·2026
Same author

Developing and validating a sequence-aware deep learning model for infection risk prediction in home care.

International journal of medical informatics·2026
Same author

Consequences of compassion fatigue in palliative care nurses: the experience of meaninglessness and emptiness in life.

BMC palliative care·2026
Same author

Large Language Model Adaptation Strategies in Speech-Based Cognitive Screening: Systematic Evaluation.

JMIR AI·2026

Related Experiment Video

Updated: Jun 10, 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

Computer-aided intelligent system for diagnosing pediatric asthma.

Maryam Zolnoori1, Mohammad Hossein Fazel Zarandi, Mostafa Moin

  • 1Academic Center for Education, Culture and Research, Tarbiat Modares University, Tehran, Iran. M.Zolnoori@gmail.com

Journal of Medical Systems
|August 13, 2010
PubMed
Summary
This summary is machine-generated.

A new fuzzy system aids asthma diagnosis by analyzing patient-reported symptoms. This tool improves diagnostic accuracy, especially in underserved regions, by providing clear asthma possibility scores.

Related Experiment Videos

Last Updated: Jun 10, 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:

  • Medical Informatics
  • Pulmonology
  • Artificial Intelligence in Healthcare

Background:

  • Asthma is a prevalent chronic inflammatory lung disease with significant underdiagnosis, particularly in developing nations due to limited healthcare access.
  • Barriers to accurate asthma diagnosis include a lack of specialist access and inadequate laboratory facilities, leading to delayed or missed treatment.
  • Patient perception of disease symptoms is crucial but often underutilized in standard diagnostic pathways.

Purpose of the Study:

  • To develop and evaluate a novel patient-based fuzzy system designed to enhance the diagnostic process for asthma.
  • To address critical issues in asthma diagnosis, including patient-centric data representation and algorithmic inference based on patient responses.
  • To provide a user-friendly interface for data capture and generate actionable outputs for both patients and physicians regarding asthma likelihood.

Main Methods:

  • Development of a modular fuzzy system incorporating patient-reported variables and algorithmic inference.
  • Implementation of a front-end interface for efficient patient data collection.
  • Evaluation of the system's efficacy using a study sample of 139 asthmatic and 139 non-asthmatic children (age 6-18).

Main Results:

  • The developed fuzzy system demonstrated high diagnostic performance in the study sample.
  • The system achieved a sensitivity of 88% and a specificity of 100% for asthma diagnosis at a cutoff value of 0.7.
  • The system provides an output score (0-10) indicating the possibility of asthma for both patients and physicians.

Conclusions:

  • The patient-based fuzzy system represents a promising tool for improving asthma diagnosis, particularly in resource-limited settings.
  • The system's high sensitivity and specificity suggest its potential to reduce underdiagnosis and facilitate timely intervention.
  • This approach leverages patient perception and algorithmic analysis to offer a more accessible and effective method for asthma assessment.