Chronic Obstructive Pulmonary Disease (COPD)NCT07010211
Artificial Intelligence-Based Motion Analysis for Early Detection of COPD
An observational study of Chronic Obstructive Pulmonary Disease (COPD), run by Burcin Celik.
Trial summary
This study aims to develop a non-invasive and contact-free diagnostic system that uses artificial intelligence (AI) to detect Chronic Obstructive Pulmonary Disease (COPD) by analyzing walking patterns. Participants in this study will include individuals with a diagnosis of COPD and healthy volunteers. Read more about this study
- Who is it for?
- Ages 40 to 80 yearsHealthy volunteers can take part
- Conditions the study is for
- Chronic Obstructive Pulmonary Disease (COPD)
- What is being tested?
- Gait Video Recording and Analysis
- How many people
- 56 participants
- Where
- No sites listed yet
- Study dates
- 1 Aug 2025to 1 Mar 2026
- 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 40 to 80 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: This study will include individuals between the ages of 40 and 80. The study population consists of two cohorts: patients previously diagnosed with Chronic Obstructive Pulmonary Disease (COPD) based on spirometry results, and healthy volunteers with no history of pulmonary disease. All participants must be physically able to complete a 6-minute walk test and willing to participate in video-based gait assessment.
Who may be able to join
- Aged between 40 and 80 years
- Ability to provide informed consent
- For COPD group: Previously diagnosed with COPD based on GOLD criteria (FEV1/FVC < 0.70)
- For control group: No history of pulmonary disease and normal spirometry results
- Physically able to perform the 6-minute walk test
- Willingness to participate in video recording during gait analysis
Who cannot join
- Younger than 40 or older than 80 years
- Acute respiratory tract infection or other active infections
- Severe heart failure, advanced arrhythmias, or other serious cardiovascular conditions
- Physical disability preventing completion of the 6-minute walk test
- Neurological or orthopedic conditions causing major gait disturbance
- Inability to perform spirometry due to physical or cognitive limitations
- Pregnant or breastfeeding women Diagnosed with other serious pulmonary diseases (e.g., interstitial lung disease, active tuberculosis) Refusal to give informed consent or to be video recorded
About this study
Official title: Development of an Artificial Intelligence-Based Motion Analysis System for the Detection of Chronic Obstructive Pulmonary Disease (COPD)
This study aims to develop a non-invasive and contact-free diagnostic system that uses artificial intelligence (AI) to detect Chronic Obstructive Pulmonary Disease (COPD) by analyzing walking patterns.
Participants in this study will include individuals with a diagnosis of COPD and healthy volunteers. All participants will undergo a 6-minute walk test (6MWT), during which their movements will be recorded using video. In addition, they will complete a breathing test (spirometry) and a short questionnaire about symptoms.
Read more
The recorded videos will be analyzed using an AI model based on motion tracking software. This model will evaluate walking-related parameters such as step count, step length, walking time, and total walking distance. The goal is to determine whether walking patterns can be used to detect COPD with high accuracy, especially in situations where traditional lung function tests may not be available or feasible.
This study is observational and does not involve any experimental drug or treatment. The results may help to create new diagnostic tools that are easy to use, safe, and accessible for early detection of COPD.
- Being tested
- Gait Video Recording and Analysis
- Keywords
- Chronic Obstructive Pulmonary Disease, Artificial Intelligence, Motion Analysis, 6-Minute Walk Test, Spirometry
- 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.
Gait Video Recording and AnalysisOther
Participants undergo a 6-minute walk test (6MWT) while being recorded on video. The footage is later analyzed using artificial intelligence algorithms to assess gait parameters.
| Study group | Role | What they receive |
|---|---|---|
| COPD Group | Participants with a confirmed diagnosis of Chronic Obstructive Pulmonary Disease (COPD) based on spirometry. Receives: Other: Gait Video Recording and Analysis | |
| Control Group | Healthy volunteers with no history of pulmonary disease and normal spirometry results. Receives: Other: Gait Video Recording and Analysis |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic Accuracy of AI-Based Gait Analysis for Detection of COPD
Evaluation of the sensitivity, specificity, and overall accuracy of the artificial intelligence-based motion analysis system in identifying patients with COPD compared to spirometry (gold standard).
Measured: At time of initial assessment (Day 0)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Jun 2025
- Study start1 Aug 2025 (estimated)
- Primary completion1 Feb 2026 (estimated)
- Study completion1 Mar 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Chronic Obstructive Pulmonary Disease (COPD)Studies 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 56 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Burcin Celik
- Sponsor type
- University, hospital or other
- Collaborators
- Ondokuz Mayıs University
- Responsible party
- Sponsor-investigator, Burcin Celik, Ondokuz Mayıs University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07010211
- Sponsor's protocol ID
- B.30.2.ODM.0.20.08/220
- Status last verified
- May 2025
- First submitted
- 30 May 2025
- Last updated
- 8 Jun 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Individual participant data (IPD) that underlie the results reported in this study will be shared with qualified researchers upon reasonable request. Data will be de-identified to protect participant confidentiality and shared for academic research purposes only, in accordance with institutional ethics approval and data protection policies.
Publications
Altan G, Kutlu Y, Allahverdi N. Deep Learning on Computerized Analysis of Chronic Obstructive Pulmonary Disease. IEEE J Biomed Health Inform. 2019 Jul 26. doi: 10.1109/JBHI.2019.2931395. Online ahead of print.
PubMed 31369388Agusti A, Celli BR, Criner GJ, Halpin D, Anzueto A, Barnes P, Bourbeau J, Han MK, Martinez FJ, Montes de Oca M, Mortimer K, Papi A, Pavord I, Roche N, Salvi S, Sin DD, Singh D, Stockley R, Lopez Varela MV, Wedzicha JA, Vogelmeier CF. Global Initiative for Chronic Obstructive Lung Disease 2023 Report: GOLD Executive Summary. Eur Respir J. 2023 Apr 1;61(4):2300239. doi: 10.1183/13993003.00239-2023. Print 2023 Apr.
PubMed 36858443
Study at a glance
From the registry record, last updated by the sponsor on 8 Jun 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of an Artificial Intelligence-Based Motion Analysis System for the Detection of Chronic Obstructive Pulmonary Disease (COPD)
- ClinicalTrials.gov ID
- NCT07010211
- Acronym / sponsor ID
- B.30.2.ODM.0.20.08/220
- Keywords
- Chronic Obstructive Pulmonary Disease, Artificial Intelligence, Motion Analysis, 6-Minute Walk Test, Spirometry
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 56 participants (estimated)
- Lead sponsor
- Burcin CelikUniversity, hospital or other
- Collaborators
- Ondokuz Mayıs University
- Study start
- 1 Aug 2025 (estimated)
- Primary completion
- 1 Feb 2026 (estimated)
- Study completion
- 1 Mar 2026 (estimated)
- First posted
- 8 Jun 2025 (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 study aims to develop a non-invasive and contact-free diagnostic system that uses artificial intelligence (AI) to detect Chronic Obstructive Pulmonary Disease (COPD) by analyzing walking patterns.
Participants in this study will include individuals with a diagnosis of COPD and healthy volunteers. All participants will undergo a 6-minute walk test (6MWT), during which their movements will be recorded using video. In addition, they will complete a breathing test (spirometry) and a short questionnaire about symptoms.
The recorded videos will be analyzed using an AI model based on motion tracking software. This model will evaluate walking-related parameters such as step count, step length, walking time, and total walking distance. The goal is to determine whether walking patterns can be used to detect COPD with high accuracy, especially in situations where traditional lung function tests may not be available or feasible.
This study is observational and does not involve any experimental drug or treatment. The results may help to create new diagnostic tools that are easy to use, safe, and accessible for early detection of COPD.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| COPD Group | – | Other: Gait Video Recording and AnalysisParticipants with a confirmed diagnosis of Chronic Obstructive Pulmonary Disease (COPD) based on spirometry. |
| Control Group | – | Other: Gait Video Recording and AnalysisHealthy volunteers with no history of pulmonary disease and normal spirometry results. |
Who may qualify
- Ages
- 40 to 80 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- This study will include individuals between the ages of 40 and 80. The study population consists of two cohorts: patients previously diagnosed with Chronic Obstructive Pulmonary Disease (COPD) based on spirometry results, and healthy volunteers with no history of pulmonary disease. All participants must be physically able to complete a 6-minute walk test and willing to participate in video-based gait assessment.
Key inclusion criteria (6 listed)
- Aged between 40 and 80 years
- Ability to provide informed consent
- For COPD group: Previously diagnosed with COPD based on GOLD criteria (FEV1/FVC < 0.70)
- For control group: No history of pulmonary disease and normal spirometry results
- Physically able to perform the 6-minute walk test
Key exclusion criteria (7 listed)
- Younger than 40 or older than 80 years
- Acute respiratory tract infection or other active infections
- Severe heart failure, advanced arrhythmias, or other serious cardiovascular conditions
- Physical disability preventing completion of the 6-minute walk test
- Neurological or orthopedic conditions causing major gait disturbance
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 |
|---|---|---|
| Gait Video Recording and AnalysisParticipants undergo a 6-minute walk test (6MWT) while being recorded on video. The footage is later analyzed using artificial intelligence algorithms to assess gait parameters. | Other | COPD Group, Control Group |
Primary outcome
Diagnostic Accuracy of AI-Based Gait Analysis for Detection of COPD
Time frame: At time of initial assessment (Day 0)
Evaluation of the sensitivity, specificity, and overall accuracy of the artificial intelligence-based motion analysis system in identifying patients with COPD compared to spirometry (gold standard).
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 8 Jun 2025.
- Results publication: PubMed 31369388Altan G, Kutlu Y, Allahverdi N. Deep Learning on Computerized Analysis of Chronic Obstructive Pulmonary Disease. IEEE J Biomed Health Inform. 2019 Jul 26. doi: 10.1109/JBHI.2019.2931395. Online ahead of print.
- Results publication: PubMed 36858443Agusti A, Celli BR, Criner GJ, Halpin D, Anzueto A, Barnes P, Bourbeau J, Han MK, Martinez FJ, Montes de Oca M, Mortimer K, Papi A, Pavord I, Roche N, Salvi S, Sin DD, Singh D, Stockley R, Lopez Varela MV, Wedzicha JA, Vogelmeier CF. Global Initiative for Chronic Obstructive Lung Disease 2023 Report: GOLD Executive Summary. Eur Respir J. 2023 Apr 1;61(4):2300239. doi: 10.1183/13993003.00239-2023. Print 2023 Apr.
- Individual participant dataSharing planned: Yes. Individual participant data (IPD) that underlie the results reported in this study will be shared with qualified researchers upon reasonable request. Data will be de-identified to protect participant confidentiality and shared for academic research purposes only, in accordance with institutional ethics approval and data protection policies.
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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.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Chronic Obstructive Pulmonary Disease (COPD), run by Burcin Celik.
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 6 inclusion and 7 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: opening soon, as of the sponsor's last update on 8 Jun 2025. Status can change between updates, so confirm with the study contact before making plans.
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?
Diagnostic Accuracy of AI-Based Gait Analysis for Detection of COPD (time frame: At time of initial assessment (Day 0)). 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 NCT07010211, which also lists 2 linked publications.
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 8 Jun 2025.