Feasibility of Artificial Intelligence-based Heart Function Prediction Model Using Chest Radiography
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Chest X-ray for Clinical Evaluation
- Sponsor
- Yonsei University
- Enrollment
- 505
- Locations
- 1
- Primary Endpoint
- Left Ventricular Ejection Fraction < 40%
- Status
- Completed
- Last Updated
- 3 years ago
Overview
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
Investigators
SungA Bae
MD. PhD.
Yonsei University
Eligibility Criteria
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
Outcomes
Primary Outcomes
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%)