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| Name | Class |
|---|---|
| Saglik Bilimleri Universitesi | OTHER |
| Yedikule Training and Research Hospital | OTHER |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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 |
| |
| Healthy Group | Healthy participants Adults aged between 18-65 years of age with good general health |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Recording voice samples | Other | Voice recording with
|
|
| Measure | Description | Time Frame |
|---|---|---|
| Comparison of voice characteristics | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Classification of voice characteristics | 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. |
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Asthmatic Group
Inclusion Criteria:
Exclusion Criteria:
Healthy Group
Inclusion Criteria:
Exclusion Criteria:
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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'.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Health Sciences Yedikule Chest Diseases and Thoracic Surgery Training And Reseaerch Hospital | Istanbul | 34100 | Turkey (Türkiye) |
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| ID | Term |
|---|---|
| D001249 | Asthma |
| ID | Term |
|---|---|
| D001982 | Bronchial Diseases |
| D012140 | Respiratory Tract Diseases |
| D008173 | Lung Diseases, Obstructive |
| D008171 | Lung Diseases |
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| D012130 |
| Respiratory Hypersensitivity |
| D006969 | Hypersensitivity, Immediate |
| D006967 | Hypersensitivity |
| D007154 | Immune System Diseases |