Feasibility of AI-based Heart Function Prediction Model Using CXR
NCT ID: NCT04996381
Last Updated: 2022-09-14
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
505 participants
OBSERVATIONAL
2022-03-01
2022-09-01
Brief Summary
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Detailed Description
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Primary Objective: Use chest radiographs to predict the left ventricular ejection fraction
Conditions
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Study Design
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COHORT
PROSPECTIVE
Interventions
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Scanning Chest X-rays and performing AI algorithms on images
Chest X-Rays; AI CNNs; Results
Eligibility Criteria
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Inclusion Criteria
* Patient who visited the emergency room or outpatient clinic due to dyspnea and chest pain
Exclusion Criteria
* Uncertain radiographs or transthoracic echocardiography
* Uncertain tests results
20 Years
90 Years
ALL
No
Sponsors
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Yonsei University
OTHER
Responsible Party
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SungA Bae
MD. PhD.
Principal Investigators
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In Hyun Jung, MD, PhD
Role: STUDY_CHAIR
Yongin Severance Hospital, Yonsei University College of Medicine
Locations
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Yongin Severance Hospital
Yŏngin, Giheung-gu, South Korea
Countries
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Other Identifiers
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YonseiU
Identifier Type: -
Identifier Source: org_study_id
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