Voice Analysis in Asthmatic Patients With Machine Learning Models

N/ACompletedNCT06820671

MED-CASE344 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

344

Conditions

Asthma

Interventions

Recording voice samplesOTHER

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

Eligibility

Sex: ALLMin age: 18 YearsMax age: 65 YearsHealthy volunteers:

Medical Language ↔ Plain English

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

Locations (1)

University of Health Sciences Yedikule Chest Diseases and Thoracic Surgery Training And Reseaerch Hospital

Istanbul, Turkey (Türkiye)

Outcomes

Primary Outcomes

Comparison of voice characteristics

Comparison of voice characteristics in asthmatic patients and healthy individuals with machine learning and deep learning

Time frame: One session, a maximum of 7 voice sample recording in one session for each participant, 2 minutes total.

Secondary Outcomes

Classification of voice characteristics

Classification of voice characteristics according to Global Initiative for Asthma - (GINA) criteria using machine learning and deep learning

Time frame: A maximum of 7 voice sample recording in one session for each participant, 2 minutes total.

Data from ClinicalTrials.gov

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