Chronic Obstructive Pulmonary DiseaseNCT06160674
Vowel Segmentation for Classification of Chronic Obstructive Pulmonary Disease Using Machine Learning
An observational study of Chronic Obstructive Pulmonary Disease, run by Blekinge Institute of Technology at 1 site in Sweden.
Trial summary
This work aims to evaluate whether the segmentation of vowel recordings collected from patients diagnosed with COPD and healthy control groups can increase the classification precision of machine learning techniques. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Chronic Obstructive Pulmonary Disease
- What is being tested?
- COPD
- How many people
- 68 participants
- Where
- 1 siteSweden
- Study dates
- 28 Nov 2023to 30 Nov 2024
- How long will I take part?
- 6 Months
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Data will be collected from participants 18 years old and older with and without COPD diagnosis will be recruited.
Who may be able to join
- being 18 years old and older.
Who cannot join
- being under 18 years old and older.
Trial locations and contacts
1 site in 1 country.
Sweden1 site
Blekinge Institute of Technology
Karlskrona, Blekinge County, 37179
About this study
This work aims to evaluate whether the segmentation of vowel recordings collected from patients diagnosed with COPD and healthy control groups can increase the classification precision of machine learning techniques.
Read the detailed description
Voice data and sociodemographic data on gender and age will be collected through the "VoiceDiganostic" application from the company Voice Diagnostic. Collected vowel recordings will be segmented and tested to determine whether some segments contain more information for the discrimination of COPD from healthy control groups.
Each segment 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 each segment which in turn will be evaluated for classification performance using several machine learning algorithms.
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.
- Conditions
- Chronic Obstructive Pulmonary Disease
- Being tested
- COPD
- Keywords
- Segmentation, Classification, COPD, Machine Learning
- Medical subject headings
- Pulmonary Disease, Chronic Obstructive
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
COPDOther
Also called HC
A vowel segmentation data set consisting of information from COPD and HC groups will be used to experiment with the classification performance of several Machine Learning techniques on different segments of a vowel recording.
| Study group | Role | What they receive |
|---|---|---|
| COPD | 30 COPD participants, 16 Female and 14 Male. Receives: Other: COPD | |
| HC | 38 HC participants, 20 Female and 18 Male. Receives: Other: COPD |
What is being measured
The primary outcome is the main question the study is designed to answer.
Classification performance
Binary classification performance of the ML algorithm on each segment.
Measured: 30 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Dec 2023
- Study start28 Nov 2023
- Primary completion30 Oct 2024
- Study completion30 Nov 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Chronic Obstructive Pulmonary DiseaseStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Enrolment
- 68 participants (actual)
- Target duration
- 6 Months
Sponsor and investigators
- Lead sponsor
- Blekinge Institute of Technology
- Sponsor type
- University, hospital or other
- Collaborators
- Excellence Center at Linköping - Lund in Information Technology (ELLIIT)
- Responsible party
- Sponsor
- Investigators
- Johan Sanmartin Berglund, MD, PhD, Principal investigator, Blekinge Institute of Technology
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06160674
- Sponsor's protocol ID
- BTH-6.1.1-0169-2023
- Status last verified
- Nov 2024
- First submitted
- 28 Nov 2023
- Last updated
- 25 Nov 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Participant data can not be shared due to the GDPR. However, the dataset created can be available upon request from the institution.
Publications
Idrisoglu A, Moraes ALD, Cheddad A, Anderberg P, Jakobsson A, Berglund JS. Vowel segmentation impact on machine learning classification for chronic obstructive pulmonary disease. Sci Rep. 2025 Mar 22;15(1):9930. doi: 10.1038/s41598-025-95320-3.
PubMed 40121302
Study at a glance
From the registry record, last updated by the sponsor on 25 Nov 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Vowel Segmentation for Classification of Chronic Obstructive Pulmonary Disease Using Machine Learning
- ClinicalTrials.gov ID
- NCT06160674
- Acronym / sponsor ID
- BTH-6.1.1-0169-2023
- Conditions
- Chronic Obstructive Pulmonary Disease
- Keywords
- Segmentation, Classification, COPD, Machine Learning
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 68 participants (actual)
- Lead sponsor
- Blekinge Institute of TechnologyUniversity, hospital or other
- Collaborators
- Excellence Center at Linköping - Lund in Information Technology (ELLIIT)
- Study start
- 28 Nov 2023 (actual)
- Primary completion
- 30 Oct 2024 (actual)
- Study completion
- 30 Nov 2024 (estimated)
- First posted
- 7 Dec 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This work aims to evaluate whether the segmentation of vowel recordings collected from patients diagnosed with COPD and healthy control groups can increase the classification precision of machine learning techniques.
Detailed description
Voice data and sociodemographic data on gender and age will be collected through the "VoiceDiganostic" application from the company Voice Diagnostic. Collected vowel recordings will be segmented and tested to determine whether some segments contain more information for the discrimination of COPD from healthy control groups.
Each segment 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 each segment which in turn will be evaluated for classification performance using several machine learning algorithms.
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.
Study design
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Target follow-up
- 6 Months
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| COPD | – | Other: COPD30 COPD participants, 16 Female and 14 Male. |
| HC | – | Other: COPD38 HC participants, 20 Female and 18 Male. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Data will be collected from participants 18 years old and older with and without COPD diagnosis will be recruited.
Key inclusion criteria (1 listed)
- being 18 years old and older.
Key exclusion criteria (1 listed)
- being under 18 years old and older.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| COPDAlso: HCA vowel segmentation data set consisting of information from COPD and HC groups will be used to experiment with the classification performance of several Machine Learning techniques on different segments of a vowel recording. | Other | COPD, HC |
Primary outcome
Classification performance
Time frame: 30 weeks
Binary classification performance of the ML algorithm on each segment.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Nov 2024.
- Derived from this study: PubMed 40121302Idrisoglu A, Moraes ALD, Cheddad A, Anderberg P, Jakobsson A, Berglund JS. Vowel segmentation impact on machine learning classification for chronic obstructive pulmonary disease. Sci Rep. 2025 Mar 22;15(1):9930. doi: 10.1038/s41598-025-95320-3.
- Individual participant dataSharing planned: No. Participant data can not be shared due to the GDPR. However, the dataset created can be available upon request from the institution.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Johan Sanmartin Berglund, MD, PhD principal investigator
Blekinge Institute of Technology
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Chronic Obstructive Pulmonary Disease, run by Blekinge Institute of Technology at 1 site in Sweden.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 25 Nov 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Sweden. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Classification performance (time frame: 30 weeks). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06160674, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Nov 2024; AzTrials last refreshed its copy on 31 Oct 2026.