- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT04996381
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
-
-
Giheung-gu
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Yongin, Giheung-gu, Korea, Republic of, 16995
- Yongin Severance Hospital
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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
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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