Artificial Intelligence-Based Motion Analysis for Early Detection of COPD

NCT07010211 | Not Yet Recruiting

• 1 other identifier: B.30.2.ODM.0.20.08/220
• Study Type: observational
• Target: 56
• Locations: 0 countries
• Sites: N/A

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.

Conditions

Chronic Obstructive Pulmonary Disease (COPD)

Keywords

Chronic Obstructive Pulmonary Disease; Artificial Intelligence; Motion Analysis; 6-Minute Walk Test; Spirometry

Outcome Measures

Primary Outcomes (1)

• 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).

  At time of initial assessment (Day 0)

Study Arms (2)

COPD Group

Participants with a confirmed diagnosis of Chronic Obstructive Pulmonary Disease (COPD) based on spirometry.

Other: Gait Video Recording and Analysis

Control Group

Healthy volunteers with no history of pulmonary disease and normal spirometry results.

Other: Gait Video Recording and Analysis

Interventions

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.

COPD Group; Control Group

Eligibility Criteria

• Age: 40 Years - 80 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Burcin Celik: lead
• Ondokuz Mayıs University: collaborator

Related Publications (2)

• 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 BACKGROUND

• 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.

  PMID: 36858443 BACKGROUND

MeSH Terms

Conditions

Pulmonary Disease, Chronic Obstructive

Condition Hierarchy (Ancestors)

Lung Diseases, Obstructive; Lung Diseases; Respiratory Tract Diseases; Chronic Disease; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Professor of Thoracic Surgery

Study Record Dates

• First Submitted: May 30, 2025
• First Posted: June 8, 2025
• Study Start: August 1, 2025
• Primary Completion: February 1, 2026
• Study Completion: March 1, 2026
• Last Updated: June 8, 2025

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will share