Clinical trial NCT06820671: Voice Analysis in Asthmatic Patients With Machine Learning Models
Official title: Voice Analysis in Asthmatic Patients and Healthy Individuals: Comparative Evaluation of Asthma Control Levels and Voice Characteristics With Machine Learning Models
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Asthma
Keywords: voice biomarker
machine learning
Lead sponsor: MED-CASE
Sponsor class: OTHER
Collaborators: name: Saglik Bilimleri Universitesi
name: Yedikule Training and Research Hospital
First posted: 2025-02-11
Enrollment: 344
Sex: ALL
Minimum age (years): 18
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: CROSS_SECTIONAL
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'.

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.

Eligibility 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:

* None

Primary outcomes:
measure: Comparison of voice characteristics
timeFrame: One session, a maximum of 7 voice sample recording in one session for each participant, 2 minutes total.
Secondary outcomes:
measure: Classification of voice characteristics
timeFrame: A maximum of 7 voice sample recording in one session for each participant, 2 minutes total.
Other outcomes:
Locations:
city: Istanbul
country: Turkey
facility: University of Health Sciences Yedikule Chest Diseases and Thoracic Surgery Training And Reseaerch Hospital
Study officials:
References: