Feasibility of AI-based Heart Function Prediction Model Using CXR

Yonsei University505 enrolled

Overview

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

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

505

Conditions

Chest X-ray for Clinical Evaluation

Interventions

Scanning Chest X-rays and performing AI algorithms on imagesDIAGNOSTIC_TEST

Chest X-Rays; AI CNNs; Results

Eligibility

Sex: ALLMin age: 20 YearsMax age: 90 Years

Medical Language ↔ Plain English

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

Locations (1)

Yongin Severance Hospital

Yŏngin, Giheung-gu, South Korea

Outcomes

Primary Outcomes

Left Ventricular Ejection Fraction < 40%

Evaluate the performance of chest X-ray based artificial intelligence algorithms to identify individuals with reduced ejection fraction (\<40%)

Time frame: Within two weeks of chest X-ray

Data from ClinicalTrials.gov

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