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OpenTrials
Completed

NCT Number: NCT05897944

Creating and Assessing a Voice Dataset for Automated Classification of Chronic Obstructive Pulmonary Disease

This work aims to evaluate whether voice recordings collected from patients diagnosed with COPD and healthy control groups can be used to detect the disease using machine learning techniques.

Completed

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Key information

Conditions

Chronic Obstructive Pulmonary Disease Chronic Disease Disease Attributes Lung Diseases Lung Diseases, Obstructive Pathologic Processes Pathological Conditions, Signs and Symptoms Pulmonary Disease, Chronic Obstructive Respiratory Tract Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Blekinge Institute of Technology

Karlskrona, Blekinge County, 37179, Sweden

About this study

Voice data and sociodemographic data on gender and age will be collected through the "VoiceDiganostic" application from the company Voice Diagnostic, which allows one to participate without location dependency. Participants with a diagnosis will be marked as the COPD group, and others will be marked as the healthy control group. Private information such as known comorbidities, personal security numbers, health parameters and communication information will be separately noticed in a participation table for each group.

The collected data will be transformed into mathematical vocal measures called voice features. A dataset consisting of voice features in conjunction with demographics and health data will be constructed for further usage as an input to ML techniques.

Descriptive statistical analysis will be held on attributes containing information on input data and gained outcomes from ML algorithms. The achieved results will be presented in the form of summary tables and graphs.

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • being 18 years old and older.

Exclusion criteria

  • being under 18 years old.

Treatment and study plan

COPD

Other

A data set consisting of information from COPD and HC groups will be used to experiment with the classification performance of several Machine Learning techniques.

Other names: HC

Primary outcomes

  1. Accuracy

    Time frame: Week 51

    Binary detection performance of the ML algorithm

  2. Input data importance scale

    Time frame: Week 51

    Features used as input data will be ranked from most important to less important one.

Sponsors and collaborators

Lead sponsor

Blekinge Institute of Technology

Other

Collaborators

  • Excellence Center at Linköping - Lund in Information Technology (ELLIIT)

Registry information

Important dates

Study start
2021
Primary completion
2024
Study completion
2024
First posted
Jun 9, 2023
Registry last updated
Mar 19, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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