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.

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.

Inclusion Criteria:

• 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)

Exclusion Criteria:

• 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

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.