Feasibility of AI-based Heart Function Prediction Model Using CXR (AI-CXR)

September 12, 2022 updated by: SungA Bae, Yonsei University

Feasibility of Artificial Intelligence-based Heart Function Prediction Model Using Chest Radiography

The investigators will develop an artificial intelligence model to predict left ventricular ejection fraction using chest radiographic images and transthoracic echocardiography data.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

Echocardiography should be considered at an early stage in patients who have first developed heart failure or who do not have information about heart function, but the examination may be delayed due to lack of time and manpower in the actual medical field.

Primary Objective: Use chest radiographs to predict the left ventricular ejection fraction

Study Type

Observational

Enrollment (Actual)

505

Contacts and Locations

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

Study Locations

  • Korea, Republic of
    • Giheung-gu
      • Yongin, Giheung-gu, Korea, Republic of, 16995
        • Yongin Severance Hospital

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

20 years to 90 years (ADULT, OLDER_ADULT)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Patients undergoing a transthoracic echocardiogram will be enrolled.

Description

Inclusion Criteria:

  • Adults who are 20 years and older
  • Patient who visited the emergency room or outpatient clinic due to dyspnea and chest pain

Exclusion Criteria:

  • Patient refusal
  • Uncertain radiographs or transthoracic echocardiography
  • Uncertain tests results

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
Left Ventricular Ejection Fraction < 40%
Time Frame: Within two weeks of chest X-ray
Evaluate the performance of chest X-ray based artificial intelligence algorithms to identify individuals with reduced ejection fraction (<40%)
Within two weeks of chest X-ray

Collaborators and Investigators

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

Sponsor

Yonsei University

Investigators

  • Study Chair: In Hyun Jung, MD, PhD, Yongin Severance Hospital, Yonsei University College of Medicine

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)

March 1, 2022

Primary Completion (ACTUAL)

June 30, 2022

Study Completion (ACTUAL)

September 1, 2022

Study Registration Dates

First Submitted

August 4, 2021

First Submitted That Met QC Criteria

August 4, 2021

First Posted (ACTUAL)

August 9, 2021

Study Record Updates

Last Update Posted (ACTUAL)

September 14, 2022

Last Update Submitted That Met QC Criteria

September 12, 2022

Last Verified

September 1, 2022

More Information

Terms related to this study

Other Study ID Numbers

  • YonseiU

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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