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Clinical Trial NCT07010211 for Chronic Obstructive Pulmonary Disease (COPD) is not yet recruiting. See the Trial Radar Card View and AI discovery tools for all the details. Or ask anything here.
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Artificial Intelligence-Based Motion Analysis for Early Detection of COPD 56 Non-Invasive Observational

Not yet recruiting
Clinical Trial NCT07010211 is an observational study for Chronic Obstructive Pulmonary Disease (COPD) and is currently not yet recruiting. Enrollment is planned to begin on 1 August 2025 and continue until the study accrues 56 participants. Led by Burcin Celik, this study is expected to complete by 1 March 2026. The latest data from ClinicalTrials.gov was last updated on 8 June 2025.
Brief 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. All participants will undergo a 6-minute walk test (6MWT), during which their movements will be recorded using video...

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Official Title

Development of an Artificial Intelligence-Based Motion Analysis System for the Detection of Chronic Obstructive Pulmonary Disease (COPD)

Conditions
Chronic Obstructive Pulmonary Disease (COPD)
Publications
Scientific articles and research papers published about this clinical trial:
Other Study IDs
  • B.30.2.ODM.0.20.08/220
NCT ID Number
NCT07010211
Start Date (Actual)
2025-08-01
Last Update Posted
2025-06-08
Completion Date (Estimated)
2026-03-01
Enrollment (Estimated)
56
Study Type
Observational
Status
Not yet recruiting
Keywords
Chronic Obstructive Pulmonary Disease
Artificial Intelligence
Motion Analysis
6-Minute Walk Test
Spirometry
Arms / Interventions
Participant Group/ArmIntervention/Treatment
COPD Group
Participants with a confirmed diagnosis of Chronic Obstructive Pulmonary Disease (COPD) based on spirometry.
Gait Video Recording and Analysis
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.
Control Group
Healthy volunteers with no history of pulmonary disease and normal spirometry results.
Gait Video Recording and Analysis
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.
Primary Outcome Measures
Outcome MeasureMeasure DescriptionTime Frame
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).
At time of initial assessment (Day 0)
Participation Assistant
Eligibility Criteria

Eligible Ages
Adult, Older Adult
Minimum Age
40 Years
Eligible Sexes
All
Accepts Healthy Volunteers
Yes
  • 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

  • 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
Burcin Celik logoBurcin Celik
Ondokuz Mayıs University logoOndokuz Mayıs University
Study Responsible Party
Burcin Celik, Sponsor-Investigator, Professor of Thoracic Surgery, Ondokuz Mayıs University
No contact data.