Vowel Segmentation for Classification of Chronic Obstruct... | NCT06160674

Studies
Vowel Segmentation for Classification of Chronic Obstructive Pulmonary Disease Using Machine Learning

NCT06160674

Sponsor

Blekinge Institute of Technology

Status

Active, not recruiting

Last Update Posted

Nov 25, 2024Actual

Enrollment

68Actual

Phase

Not provided

Conditions

Chronic Obstructive Pulmonary Disease

Interventions

COPD

Countries

Sweden

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Nov 2024

Overall Recruitment Status or Expanded Access Status

Active, not recruiting

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Nov 28, 2023Actual

Primary Completion Date

Oct 30, 2024Actual

Completion Date

Nov 30, 2024Estimated

First Submitted Date

Nov 28, 2023

First Submission Date that Met QC Criteria

Dec 5, 2023

First Posted Date

Dec 7, 2023Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Nov 22, 2024

Last Update Posted Date

Nov 25, 2024Actual

Name	Class
Excellence Center at Linköping - Lund in Information Technology (ELLIIT)	UNKNOWN

Oversight Module

Has Data Monitoring Committee (DMC)

Yes

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

Brief 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.

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.

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Yes

Target Follow-Up Duration

6 Months

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

68Actual

Arms/Interventions Module

Arm Groups

Label	Type	Description	Intervention Names
COPD		30 COPD participants, 16 Female and 14 Male.	Other: COPD
HC		38 HC participants, 20 Female and 18 Male.	Other: COPD

Interventions

Name	Type	Description	Arm Group Labels	Other Names
COPD	Other	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.

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
Classification performance	Binary classification performance of the ML algorithm on each segment.	30 weeks

Secondary Outcomes

Not provided

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

Inclusion Criteria:

being 18 years old and older.

Exclusion Criteria:

being under 18 years old and older.

Accepts Healthy Volunteers

Yes

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

18 Years

Maximum Age

Not provided

Standard Ages

AdultOlder Adult

Study Population

Data will be collected from participants 18 years old and older with and without COPD diagnosis will be recruited.

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Not provided

Overall Officials

Name	Affiliation	Role
Johan Sanmartin Berglund, MD, PhD	Blekinge Institute of Technology	Principal Investigator

Locations

Facility	Status	City	State	ZIP	Country	Contacts
Blekinge Institute of Technology		Karlskrona	Blekinge County	37179	Sweden

References Module

Citations

PubMed Identifier	Type	Citation	Retractions
40121302	Derived	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.

ID	Term
D008173	Lung Diseases, Obstructive
D008171	Lung Diseases
D012140	Respiratory Tract Diseases
D002908	Chronic Disease