Asthma
Conditions
Keywords
voice biomarker, machine learning
Brief summary
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.
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.
Interventions
Voice recording with * reading the standard text * repeating the test words * vowel elicitation of 'a' and 'o' vowels for 5-10 seconds
Sponsors
Saglik Bilimleri Universitesi
Yedikule Training and Research Hospital
MED-CASE
Study design
Observational model
CASE_CONTROL
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to 65 Years
Healthy volunteers
Yes
Inclusion 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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Comparison of voice characteristics | 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 |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Classification of voice characteristics | 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 |
Countries
Turkey (Türkiye)
Outcome results
None listed