Chest X-ray for Clinical Evaluation
Conditions
Brief summary
The investigators will develop an artificial intelligence model to predict left ventricular ejection fraction using chest radiographic images and transthoracic echocardiography data.
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
Interventions
DIAGNOSTIC_TESTScanning Chest X-rays and performing AI algorithms on images
Chest X-Rays; AI CNNs; Results
Sponsors
Yonsei University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
20 Years to 90 Years
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Left Ventricular Ejection Fraction < 40% | 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%) |
Countries
South Korea
Outcome results
None listed