NCT07010211

Artificial Intelligence-Based Motion Analysis for Early Detection of COPD

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

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 56 (estimated)
• Sponsor: Burcin Celik · Academic / Other
• Sex: All
• Age: 40 Years – 80 Years
• Healthy volunteers: Accepted

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.

Conditions

• Chronic Obstructive Pulmonary Disease (COPD)

Interventions

Type	Name	Description
OTHER	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.

Timeline

• Start date: 2025-08-01
• Primary completion: 2026-02-01
• Completion: 2026-03-01
• First posted: 2025-06-08
• Last updated: 2025-06-08