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NCT06359145 Clinical Trial

Prediction of COPD Severity Using Electrical Impedance Tomography

Pulmonary Disease, Chronic Obstructive Clinical Trial

Official title:
Prediction of COPD Chest CT Severity Using Electrical Impedance Tomography by Machine Learning Methods

Clinical Trial Details — Status: Recruiting

Administrative data
NCT number NCT06359145
Other study ID # EIT and COPD
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date April 1, 2023
Est. completion date August 1, 2024

Study information
Verified date April 2024
Source Chinese PLA General Hospital
Contact Zhimei Duan, doctor
Phone 13716376758
Email 549117002@qq.com
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial 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.

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 information / eligibility
Status Recruiting
Enrollment 150
Est. completion date August 1, 2024
Est. primary completion date June 1, 2024
Accepts healthy volunteers Accepts Healthy Volunteers
Gender All
Age group 20 Years and older
Eligibility 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

Related Conditions & MeSH terms

Locations
Country Name City State
China PLA Beijing Beijing

Sponsors (1)
Lead Sponsor Collaborator
Chinese PLA General Hospital

Country where clinical trial is conducted

China, 

Outcome
Type Measure Description Time frame Safety issue
Primary The predictive power of EIT and PFT for CT visual scoring of emphysema the prediction accuracy between deep machine learning models based on PFT data and EIT data 1 mounths
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