Prediction of COPD Severity Using Electrical Impedance Tomography
Recruiting
- Conditions
- Electric ImpedanceRespiratory Function TestsPulmonary Disease, Chronic Obstructive
- Registration Number
- NCT06359145
- Lead Sponsor
- Chinese PLA General Hospital
- Brief Summary
The purpose of this study is to predict the CT visual score of emphysema with EIT-based parameters, in order to provide a non-invasive and convenient method for the evaluation of lung structure and physiological and pathological progression of COPD.
- Detailed Description
Methods: By collecting pulmonary function data, CT visual scores, and EIT data, and employing deep machine learning algorithms to compare the predictive capabilities of EIT and PFT for CT visual scores of pulmonary emphysema, this study aims to validate the ability of EIT to assess the progression of COPD.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 150
Inclusion Criteria
- Clinical physicians suspect a patient may have COPD based on symptoms and physical examination, but a definitive diagnosis has not been confirmed through PFTs.
- Age > 20 years, and be able to communicate with doctors.
- Willing to sign informed consent for the course of the study.
Exclusion Criteria
- Patient refusal of EIT examination.
- The CT scan information is incomplete, and the interval between the pulmonary function test and the CT scan is more than 180 days.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method The predictive power of EIT and PFT for CT visual scoring of emphysema 1 mounths the prediction accuracy between deep machine learning models based on PFT data and EIT data
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
PLA
🇨🇳Beijing, Beijing, China