Clinical trial NCT07010211: Artificial Intelligence-Based Motion Analysis for Early Detection of COPD
Official title: Development of an Artificial Intelligence-Based Motion Analysis System for the Detection of Chronic Obstructive Pulmonary Disease (COPD)
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Chronic Obstructive Pulmonary Disease (COPD)
Keywords: Chronic Obstructive Pulmonary Disease
Artificial Intelligence
Motion Analysis
6-Minute Walk Test
Spirometry
Lead sponsor: Ondokuz Mayıs University
Sponsor class: OTHER
Collaborators: name: Ondokuz Mayıs University
First posted: 2025-06-08
Enrollment: 56
Sex: ALL
Minimum age (years): 40
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: This study will include individuals between the ages of 40 and 80. The study population consists of two cohorts: patients previously diagnosed with Chronic Obstructive Pulmonary Disease (COPD) based on spirometry results, and healthy volunteers with no history of pulmonary disease. All participants must be physically able to complete a 6-minute walk test and willing to participate in video-based gait assessment.

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.


Eligibility criteria: 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

Primary outcomes:
measure: Diagnostic Accuracy of AI-Based Gait Analysis for Detection of COPD
timeFrame: At time of initial assessment (Day 0)
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
References:
citation: Altan G, Kutlu Y, Allahverdi N. Deep Learning on Computerized Analysis of Chronic Obstructive Pulmonary Disease. IEEE J Biomed Health Inform. 2019 Jul 26. doi: 10.1109/JBHI.2019.2931395. Online ahead of print.
citation: Agusti A, Celli BR, Criner GJ, Halpin D, Anzueto A, Barnes P, Bourbeau J, Han MK, Martinez FJ, Montes de Oca M, Mortimer K, Papi A, Pavord I, Roche N, Salvi S, Sin DD, Singh D, Stockley R, Lopez Varela MV, Wedzicha JA, Vogelmeier CF. Global Initiative for Chronic Obstructive Lung Disease 2023 Report: GOLD Executive Summary. Eur Respir J. 2023 Apr 1;61(4):2300239. doi: 10.1183/13993003.00239-2023. Print 2023 Apr.