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Artificial Intelligence-Based Motion Analysis for Early Detection of COPD

NCT07010211 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 56

Last updated 2025-06-08

No results posted yet for this study

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

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.

Sponsors & Collaborators

  • Ondokuz Mayıs University

    collaborator OTHER

  • Burcin Celik

    lead OTHER

Eligibility

Min Age
40 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2025-08-01
Primary Completion
2026-02-01
Completion
2026-03-01

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07010211 on ClinicalTrials.gov