Feasibility of AI-based Heart Function Prediction Model Using CXR
Completed
- Conditions
- Chest X-ray for Clinical Evaluation
- Registration Number
- NCT04996381
- Lead Sponsor
- Yonsei University
- 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
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 505
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 & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method 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%)
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
Yongin Severance Hospital
🇰🇷Yongin, Giheung-gu, Korea, Republic of