Trials / Recruiting
RecruitingNCT06359145
Prediction of COPD Severity Using Electrical Impedance Tomography
Prediction of COPD Chest CT Severity Using Electrical Impedance Tomography by Machine Learning Methods
- Status
- Recruiting
- Phase
- —
- Study type
- Observational
- Enrollment
- 150 (estimated)
- Sponsor
- Chinese PLA General Hospital · Academic / Other
- Sex
- All
- Age
- 20 Years
- Healthy volunteers
- Accepted
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.
Conditions
Timeline
- Start date
- 2023-04-01
- Primary completion
- 2024-06-01
- Completion
- 2024-08-01
- First posted
- 2024-04-11
- Last updated
- 2024-04-11
Locations
1 site across 1 country: China
Source: ClinicalTrials.gov record NCT06359145. Inclusion in this directory is not an endorsement.