Prediction of COPD Severity Using Electrical Impedance Tomography

April 5, 2024 updated by: wang kaifei, Chinese PLA General Hospital

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

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.

Study Overview

Status

Recruiting

Conditions

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.

Study Type

Observational

Enrollment (Estimated)

150

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Beijing
      • Beijing, Beijing, China, 100853
        • Recruiting
        • PLA
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

Clinical physicians suspect a patient may have COPD based on symptoms and physical examination, but a definitive diagnosis has not been confirmed through PFTs.

Description

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 Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

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

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Chinese PLA General Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

April 1, 2023

Primary Completion (Estimated)

June 1, 2024

Study Completion (Estimated)

August 1, 2024

Study Registration Dates

First Submitted

March 30, 2024

First Submitted That Met QC Criteria

April 5, 2024

First Posted (Actual)

April 11, 2024

Study Record Updates

Last Update Posted (Actual)

April 11, 2024

Last Update Submitted That Met QC Criteria

April 5, 2024

Last Verified

April 1, 2024

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • EIT and COPD

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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