AI-Powered Sound Analysis for COPD Screening

NCT07457073 | Active Not Recruiting

• 1 other identifier: 2026-0168
• Study Type: observational
• Target: 3,000
• Locations: 1 country
• Sites: 1

Brief Summary

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality worldwide, yet early detection remains challenging-especially in primary care settings where spirometry, the diagnostic gold standard, is often unavailable. This study aims to develop and validate a non-invasive, low-cost COPD screening tool based on artificial intelligence (AI) analysis of cough sounds. Using smartphone-recorded cough audio and clinical data from both COPD patients and non-COPD controls, the investigators will train and test an AI model to identify acoustic signatures associated with COPD. The model will be developed using a prospective cohort from Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, and externally validated in a community-based cohort across nine districts/counties in Zhejiang Province, China.

Conditions

Chronic Obstructive Pulmonary Disease (COPD)

Outcome Measures

Primary Outcomes (1)

• Diagnostic Accuracy of the AI-Based Cough Sound Model for Detecting COPD

  Sensitivity and specificity of the artificial intelligence (AI) model in identifying individuals with chronic obstructive pulmonary disease (COPD), using post-bronchodilator spirometry (FEV₁/FVC \< 0.70 according to GOLD 2024 criteria) as the reference standard.

  At the time of enrollment (single visit, baseline assessment)

Secondary Outcomes (5)

• Area Under the Receiver Operating Characteristic Curve (AUC) of the Cough Sound Model

  Baseline

• Positive and Negative Predictive Values (PPV/NPV)

  Baseline

• Correlation Between Acoustic Features and COPD Severity

  Baseline

• Model Performance Across Subgroups

  Baseline

• Feasibility of Smartphone-Based Cough Recording

  Baseline

Study Arms (2)

COPD group

Participants diagnosed with chronic obstructive pulmonary disease (COPD) according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2024 criteria, confirmed by post-bronchodilator spirometry (FEV₁/FVC \< 0.70).

Non-COPD Control Group

Participants clinically confirmed as not having COPD (post-bronchodilator FEV₁/FVC ≥ 0.70 and no clinical diagnosis of COPD).

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

This study will prospectively enroll approximately 3,000 adult participants at Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University (Hangzhou, China) - a tertiary academic medical center. This single-center cohort will serve as the primary dataset for development and internal validation of an artificial intelligence (AI)-based cough sound model for COPD screening. The cohort will include: Adults aged ≥18 years with a confirmed diagnosis of chronic obstructive pulmonary disease (COPD) based on post-bronchodilator spirometry (FEV₁/FVC \< 0.70, per GOLD 2024 criteria), and Non-COPD controls who undergo comprehensive clinical evaluation and spirometry confirming the absence of airflow limitation (FEV₁/FVC ≥ 0.70) and no clinical diagnosis of COPD.

You may qualify if:

• Age ≥18 years
• Diagnosed with COPD per GOLD 2024 criteria OR clinically confirmed as non-COPD （for COPD cohort）
• Able to perform a voluntary cough on instruction
• Provides informed consent (or through legally authorized representative/witness if illiterate)

You may not qualify if:

• Unstable angina or severe arrhythmia
• Severe fatigue due to advanced heart failure or chemotherapy
• Progressive neuromuscular disease
• Pregnancy or lactation
• Life expectancy \<6 months
• Unable to complete spirometry or study procedures
• Other vulnerable populations (e.g., active psychiatric illness, cognitive impairment, critically ill)-except elderly/illiterate individuals who are protected via consent safeguards

Sponsors & Collaborators

• Sir Run Run Shaw Hospital: lead

Study Sites (1)

Sir Run Run shaw Hospital Zhejiang University

Hangzhou, Zhejiang, 310000, China

Location

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: PRINCIPAL INVESTIGATOR
• PI Title: Director of Respiratory Department, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University

Study Record Dates

• First Submitted: March 3, 2026
• First Posted: March 9, 2026
• Study Start: February 23, 2026
• Primary Completion (Estimated): February 1, 2027
• Study Completion (Estimated): June 1, 2027
• Last Updated: March 25, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)