Artificial Intelligence-Based Motion Analysis for Early Detection of COPD

N/ANot Yet RecruitingNCT07010211

Burcin Celik56 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

Conditions

Chronic Obstructive Pulmonary Disease (COPD)

Interventions

Gait Video Recording and Analysis

Eligibility

Sex: ALLMin age: 40 YearsMax age: 80 YearsHealthy volunteers:

Medical Language ↔ Plain English

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

Outcomes

Primary Outcomes

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).

Time frame: At time of initial assessment (Day 0)