Clinical Application Study of Chronic Obstructive Pulmonary Disease Screening Using Artificial Intelligence-Based Acoustic Features
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 入组人数
- 3,000
- 试验地点
- 1
- 主要终点
- Diagnostic Accuracy of the AI-Based Cough Sound Model for Detecting COPD
研究概览
简要总结
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, we 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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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)
排除标准
- •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
研究组 & 干预措施
Non-COPD Control Group
Participants clinically confirmed as not having COPD (post-bronchodilator FEV₁/FVC ≥ 0.70 and no clinical diagnosis of COPD).
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).
结局指标
主要结局
Diagnostic Accuracy of the AI-Based Cough Sound Model for Detecting COPD
时间窗: 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.
次要结局
- Area Under the Receiver Operating Characteristic Curve (AUC) of the Cough Sound Model(Baseline)
- Positive and Negative Predictive Values (PPV/NPV)(Baseline)
- Correlation Between Acoustic Features and COPD Severity(Baseline)
- Model Performance Across Subgroups(Baseline)
- Feasibility of Smartphone-Based Cough Recording(Baseline)
研究者
Huiqing Ge
Director of Respiratory Department, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University
Sir Run Run Shaw Hospital