Chronic Obstructive Pulmonary Disease (COPD)
Conditions
Keywords
Chronic Obstructive Pulmonary Disease, Artificial Intelligence, Motion Analysis, 6-Minute Walk Test, Spirometry
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. 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.
Interventions
OTHERGait 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.
Sponsors
Ondokuz Mayıs University
Burcin Celik
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
40 Years to 80 Years
Healthy volunteers
Yes
Inclusion criteria
* 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
Exclusion criteria
* 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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic Accuracy of AI-Based Gait Analysis for Detection of COPD | 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). |
Outcome results
None listed