AI-ECG Screening for Left Ventricular Systolic Dysfunction
NCT ID: NCT06231797
Last Updated: 2024-02-05
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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UNKNOWN
1530 participants
OBSERVATIONAL
2024-02-01
2025-07-10
Brief Summary
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Detailed Description
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The investigators are planning to prospectively validate the model's effectiveness as a potential screening tool for left ventricular systolic dysfunction.
Conditions
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Study Design
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OTHER
PROSPECTIVE
Interventions
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AI algorithm conducted on 12-lead ECG and transthoracic echocardiography
12-lead ECG is performed for each patient. For 12-lead ECG, AITIALVSD (AI algorithm) analysis will be performed through a separate server.
Eligibility Criteria
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Inclusion Criteria
* Can complete both 12-lead electrocardiogram and transthoracic echocardiography
Exclusion Criteria
* Individuals who do not agree to participate in the study
* Patients who are unable to participate in clinical trials at the discretion of the investigator
18 Years
ALL
No
Sponsors
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Seoul National University Hospital
OTHER
Responsible Party
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Principal Investigators
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Seung-Pyo Lee, MD, PhD
Role: PRINCIPAL_INVESTIGATOR
Seoul National University Hospital
Central Contacts
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References
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Kwon JM, Jo YY, Lee SY, Kang S, Lim SY, Lee MS, Kim KH. Artificial Intelligence-Enhanced Smartwatch ECG for Heart Failure-Reduced Ejection Fraction Detection by Generating 12-Lead ECG. Diagnostics (Basel). 2022 Mar 8;12(3):654. doi: 10.3390/diagnostics12030654.
Kwon JM, Kim KH, Jeon KH, Kim HM, Kim MJ, Lim SM, Song PS, Park J, Choi RK, Oh BH. Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification. Korean Circ J. 2019 Jul;49(7):629-639. doi: 10.4070/kcj.2018.0446. Epub 2019 Mar 21.
Other Identifiers
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H-2306-083-1439
Identifier Type: -
Identifier Source: org_study_id
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