NCT07457073

AI-Powered Sound Analysis for COPD Screening

Clinical Application Study of Chronic Obstructive Pulmonary Disease Screening Using Artificial Intelligence-Based Acoustic Features

• Status: Active Not Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 3,000 (estimated)
• Sponsor: Sir Run Run Shaw Hospital · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

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.

Detailed description

This is a prospective observational study with a "single-center modeling + external validation" design. Two cohorts will be enrolled: (1) individuals diagnosed with COPD according to the GOLD 2024 criteria, and (2) individuals clinically confirmed as non-COPD. All participants must be ≥18 years old and able to perform a voluntary cough. Each participant will undergo standard clinical assessments-including spirometry (FEV₁, FVC, FEV₁/FVC ratio), CT imaging, blood tests, and a structured questionnaire on smoking history, respiratory symptoms, and risk factors-and will provide a 5-second cough recording via a smartphone. Audio data will be de-identified and used by Xunsheng Medical Technology Co., Ltd. to develop an AI-based screening algorithm. The primary performance metrics (sensitivity, specificity) of the cough sound model will be compared against traditional screening questionnaires using spirometry as the reference standard. The study aims to enroll approximately 3,000 participants to achieve \>90% statistical power in detecting a 10% improvement in sensitivity over questionnaire-based screening.

Conditions

• Chronic Obstructive Pulmonary Disease (COPD)

Timeline

• Start date: 2026-02-23
• Primary completion: 2027-02-01
• Completion: 2027-06-01
• First posted: 2026-03-09
• Last updated: 2026-03-25

Locations

1 site across 1 country: China