Chronic Obstructive Pulmonary Disease (COPD)NCT07457073
AI-Powered Sound Analysis for COPD Screening
An observational study of Chronic Obstructive Pulmonary Disease (COPD), run by Sir Run Run Shaw Hospital at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Chronic Obstructive Pulmonary Disease (COPD)
- How many people
- 3,000 participants
- Where
- 1 siteChina
- Study dates
- 23 Feb 2026to 1 Jun 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- 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)
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
China1 site
Sir Run Run shaw Hospital Zhejiang University
Hangzhou, Zhejiang, 310000
About this study
Official title: Clinical Application Study of Chronic Obstructive Pulmonary Disease Screening Using Artificial Intelligence-Based Acoustic Features
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.
Read the 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.
- Medical subject headings
- Pulmonary Disease, Chronic Obstructive
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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). |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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
Discriminative performance of the AI model measured by AUC, compared against COPD screening questionnaires
Measured: Baseline
Positive and Negative Predictive Values (PPV/NPV)
PPV and NPV of the AI cough sound model for COPD detection in both hospital-derived development cohort and community-based external validation cohort.
Measured: Baseline
Correlation Between Acoustic Features and COPD Severity
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.
Measured: Baseline
Model Performance Across Subgroups
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).
Measured: Baseline
Feasibility of Smartphone-Based Cough Recording
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.
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Mar 2026
- Study start23 Feb 2026
- Primary completion1 Feb 2027 (estimated)
- Study completion1 Jun 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Chronic Obstructive Pulmonary Disease (COPD)Studies of the same condition, open and under way.
- More trials run by Sir Run Run Shaw HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 3,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sir Run Run Shaw Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Huiqing Ge, Sir Run Run Shaw Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07457073
- Sponsor's protocol ID
- 2026-0168
- Status last verified
- Feb 2026
- First submitted
- 3 Mar 2026
- Last updated
- 25 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 25 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Clinical Application Study of Chronic Obstructive Pulmonary Disease Screening Using Artificial Intelligence-Based Acoustic Features
- ClinicalTrials.gov ID
- NCT07457073
- Acronym / sponsor ID
- 2026-0168
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 3,000 participants (estimated)
- Lead sponsor
- Sir Run Run Shaw HospitalUniversity, hospital or other
- Study start
- 23 Feb 2026 (actual)
- Primary completion
- 1 Feb 2027 (estimated)
- Study completion
- 1 Jun 2027 (estimated)
- First posted
- 9 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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). |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (4 listed)
- 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)
Key exclusion criteria (7 listed)
- Unstable angina or severe arrhythmia
- Severe fatigue due to advanced heart failure or chemotherapy
- Progressive neuromuscular disease
- Pregnancy or lactation
- Life expectancy <6 months
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
5 secondary outcomes
Area Under the Receiver Operating Characteristic Curve (AUC) of the Cough Sound Model
Time frame: Baseline
Positive and Negative Predictive Values (PPV/NPV)
Time frame: Baseline
Correlation Between Acoustic Features and COPD Severity
Time frame: Baseline
Model Performance Across Subgroups
Time frame: Baseline
Feasibility of Smartphone-Based Cough Recording
Time frame: Baseline
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Mar 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Chronic Obstructive Pulmonary Disease (COPD), run by Sir Run Run Shaw Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 25 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic Accuracy of the AI-Based Cough Sound Model for Detecting COPD (time frame: At the time of enrollment (single visit, baseline assessment)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Mar 2026.