Artificial Intelligence-Based Motion Analysis for Early Detection of 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.

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.

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Key information

Conditions

Chronic Obstructive Pulmonary Disease (COPD) Chronic Disease Disease Attributes Lung Diseases Lung Diseases, Obstructive Pathologic Processes Pathological Conditions, Signs and Symptoms Pulmonary Disease, Chronic Obstructive Respiratory Tract Diseases

Age range

40 year–80 year

Sex eligibility

All sexes

Study type

Observational

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Gait Video Recording and Analysis

Other

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 outcomes

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).

Sponsors and collaborators

Lead sponsor

Burcin Celik

Other

Collaborators

Ondokuz Mayıs University

Registry information

Official study title

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

Important dates

Study start: 2025
Primary completion: 2026
Study completion: 2026
First posted: Jun 8, 2025
Registry last updated: Jun 8, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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