Electric Impedance, Pulmonary Disease, Chronic Obstructive, Respiratory Function Tests
Conditions
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.
Interventions
None listed
Sponsors
Chinese PLA General Hospital
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
20 Years to No maximum
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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 |
Countries
China
Contacts
Primary ContactZhimei Duan, doctor
Outcome results
None listed