Voice Analysis in Asthmatic Patients With Machine Learning Models

May 14, 2025 updated by: MED-CASE

Voice Analysis in Asthmatic Patients and Healthy Individuals: Comparative Evaluation of Asthma Control Levels and Voice Characteristics With Machine Learning Models

Asthma can lead to various factors that impair voice production, including airway restriction, inflammation, and mucus production, resulting in changes in voice frequency and amplitude. Therefore, voice analysis may serve as an indicator of respiratory diseases.

A national, observational, case-control study is planned in Türkiye to analyze differences in voice between healthy subjects and asthmatic patients and to assess voice analysis techniques for determining an effective biomarker for asthma control using a machine learning model.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

Asthma is a disease characterized by chronic inflammation. Based on the frequency of symptoms and the use of reliever medications, the disease can be classified as either 'controlled' or 'uncontrolled'. Currently, GINA criteria and Asthma Control Test can be used to evaluate asthma control.

The relationship between respiratory functions and speech has been previously studied, revealing that voice changes can occur in asthmatic patients due to symptom presence. Asthma can lead to various factors that impair voice production, including airway restriction, inflammation, and mucus production, resulting in changes in voice frequency and amplitude. Therefore, voice analysis may serve as an indicator of respiratory diseases. Understanding the alterations in phonation/voice due to the underlying disease is crucial.

This study seeks to analyze differences in voice between healthy subjects and asthmatic patients and to assess voice analysis techniques for determining an effective biomarker for asthma control using a machine learning model.

This is a national, observational, cross-sectional study that will be conducted in Türkiye. The study consists of two stages: in the first stage, a machine learning (ML) model will be developed using voice data collected from both healthy individuals and patients diagnosed with asthma. In the second stage, this ML model will be tested to detect voice differences among patients at different levels of asthma control.

Study Type

Observational

Enrollment (Actual)

344

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Turkey
      • Istanbul, Turkey, 34100
        • University of Health Sciences Yedikule Chest Diseases and Thoracic Surgery Training And Reseaerch Hospital

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

Patients who admitted at Yedikule Chest Diseases and Thoracic Surgery Training And Research Hospital who meet eligibility criteria will be enrolled for 'Asthmatic Group'.

Healthy relatives of the patients and other healthy individuals at the hospital will be enrolled for 'Healthy Group'.

Description

Asthmatic Group

Inclusion Criteria:

  • Patients diagnosed with asthma according to GINA criteria and Pulmonary Function Test, and followed for at least three months
  • 18-65 years of age.
  • Sign an informed consent document
  • Able to comply with the study protocol during the study period.

Exclusion Criteria:

  • None

Healthy Group

Inclusion Criteria:

  • Healthy participants between 18-65 years of age
  • Good general health
  • No history of chronic respiratory disorders
  • No history of chronic systemic disorders
  • No history of upper respiratory tract infections within five days prior voice recording.

Exclusion Criteria:

  • None

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Asthmatic Group
Diagnosed asthma patients Adults aged between 18 and 65 years of age who have been diagnosed with asthma and followed-up for at least 3 months
Other: Recording voice samples

Voice recording with

  • reading the standard text
  • repeating the test words
  • vowel elicitation of 'a' and 'o' vowels for 5-10 seconds
Healthy Group
Healthy participants Adults aged between 18-65 years of age with good general health
Other: Recording voice samples

Voice recording with

  • reading the standard text
  • repeating the test words
  • vowel elicitation of 'a' and 'o' vowels for 5-10 seconds

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Comparison of voice characteristics
Time Frame: One session, a maximum of 7 voice sample recording in one session for each participant, 2 minutes total.
Comparison of voice characteristics in asthmatic patients and healthy individuals with machine learning and deep learning
One session, a maximum of 7 voice sample recording in one session for each participant, 2 minutes total.

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Classification of voice characteristics
Time Frame: A maximum of 7 voice sample recording in one session for each participant, 2 minutes total.
Classification of voice characteristics according to Global Initiative for Asthma - (GINA) criteria using machine learning and deep learning
A maximum of 7 voice sample recording in one session for each participant, 2 minutes total.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

MED-CASE

Collaborators

Saglik Bilimleri Universitesi
Yedikule Training and Research Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 9, 2024

Primary Completion (Actual)

August 5, 2024

Study Completion (Actual)

February 20, 2025

Study Registration Dates

First Submitted

January 15, 2025

First Submitted That Met QC Criteria

February 5, 2025

First Posted (Actual)

February 11, 2025

Study Record Updates

Last Update Posted (Actual)

May 18, 2025

Last Update Submitted That Met QC Criteria

May 14, 2025

Last Verified

May 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • Med-ML001

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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