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Active, Not Recruiting

NCT Number: NCT07457073

AI-Powered Sound Analysis for COPD Screening

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.

Active, Not Recruiting

This study is active but is not currently recruiting participants.

Key information

Conditions

Chronic Obstructive Pulmonary Disease (COPD) Chronic Disease Disease Attributes Lung Diseases Lung Diseases, Obstructive Pathologic Processes Pathological Conditions, Signs and Symptoms Pulmonary Disease, Chronic Obstructive Respiratory Tract Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Sir Run Run shaw Hospital Zhejiang University

Hangzhou, Zhejiang, 310000, China

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

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

    Time frame: 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 outcomes

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

    Time frame: Baseline

    Discriminative performance of the AI model measured by AUC, compared against COPD screening questionnaires

  2. Positive and Negative Predictive Values (PPV/NPV)

    Time frame: 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.

  3. Correlation Between Acoustic Features and COPD Severity

    Time frame: 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.

  4. Model Performance Across Subgroups

    Time frame: 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).

  5. Feasibility of Smartphone-Based Cough Recording

    Time frame: 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.

Sponsors and collaborators

Lead sponsor

Sir Run Run Shaw Hospital

Other

Registry information

Official study title

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

Important dates

Study start
2026
Primary completion
2027
Study completion
2027
First posted
Mar 9, 2026
Registry last updated
Mar 25, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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