Voice Analysis in Asthmatic Patients With Machine Learning Models

MED-CASE

Status

Completed

Conditions

Asthma

Treatments

Other: Recording voice samples

Study type

Observational

Funder types

Other

Identifiers

NCT06820671

Med-ML001

Details and patient eligibility

About

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.

Full 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.

Enrollment

344 patients

Sex

All

Ages

18 to 65 years old

Volunteers

Accepts Healthy Volunteers

Inclusion and exclusion criteria

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: