Chronic Obstructive Pulmonary Disease (COPD)
Conditions
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.
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.
Interventions
None listed
Sponsors
Sir Run Run Shaw Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic Accuracy of the AI-Based Cough Sound Model for Detecting COPD | At the time of enrollment (single visit, baseline assessment) | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Area Under the Receiver Operating Characteristic Curve (AUC) of the Cough Sound Model | Baseline | Discriminative performance of the AI model measured by AUC, compared against COPD screening questionnaires |
| Positive and Negative Predictive Values (PPV/NPV) | Baseline | PPV and NPV of the AI cough sound model for COPD detection in both hospital-derived development cohort and community-based external validation cohort. |
| Correlation Between Acoustic Features and COPD Severity | Baseline | Association between extracted cough acoustic biomarkers (e.g., spectral entropy, pitch, duration, harmonic-to-noise ratio) and COPD severity stages (GOLD 1-4), assessed via linear or ordinal regression models. |
| Model Performance Across Subgroups | Baseline | Sensitivity and specificity of the AI model stratified by age (\<65 vs ≥65 years), smoking status (current/former/never), and presence of comorbid respiratory conditions (e.g., asthma, bronchiectasis). |
| Feasibility of Smartphone-Based Cough Recording | Baseline | Proportion of participants able to successfully complete a high-quality 5-second voluntary cough recording using a standard smartphone under real-world primary care or hospital settings. |
Countries
China
Outcome results
None listed