Artificial Intelligence-Based Motion Analysis for Early Detection of COPD
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Gait Video Recording and Analysis.
- Who it may be relevant to
- Registry conditions: Chronic Obstructive Pulmonary Disease (COPD). Basic parameters: 40 years — 80 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
Development of an Artificial Intelligence-Based Motion Analysis System for the Detection of Chronic Obstructive Pulmonary Disease (COPD)
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.
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.
Primary outcome measures
- Diagnostic Accuracy of AI-Based Gait Analysis for Detection of COPD [Time frame: At time of initial assessment (Day 0)]
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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Cohort
Study locations
Center list to be confirmed — check the primary protocol.
Publications
- 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. PMID 31369388
- 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. PMID 36858443
Identifiers
NCT: NCT07010211 · B.30.2.ODM.0.20.08/220