Artificial Intelligence-Based Motion Analysis for Early Detection of COPD

Not yet recruiting

• Conditions: Chronic Obstructive Pulmonary Disease (COPD)

• Registration Number: NCT07010211
• Lead Sponsor: Burcin Celik

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.

Detailed Description

Not available

Study Timeline

• Status Verified: 2025-05
• Study First Submitted: 2025-05-30
• Study First Submitted QC: 2025-05-30
• Last Update Submitted: 2025-05-30
• Study First Posted: 2025-06-08
• Last Update Posted: 2025-06-08
• Study Start: 2025-08-01
• Primary Completion: 2026-02-01
• Study Completion: 2026-03-01

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 56

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
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).