Artificial Intelligence-assisted Diagnosis and Prognostication in Low Ejection Fraction Using Electrocardiograms
NCT ID: NCT05117970
Last Updated: 2024-10-23
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
NA
13631 participants
INTERVENTIONAL
2021-12-09
2023-12-31
Brief Summary
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Detailed Description
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Conditions
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Study Design
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RANDOMIZED
PARALLEL
SCREENING
NONE
Study Groups
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Intervention
Patients randomized to intervention will have access to the screening tool.
AI-enabled ECG-based Screening Tool
Primary care clinicians in the intervention group had access to the report, which displayed whether the AI-ECG result was positive or negative.The system will send a message to corresponding physicians if positive finding.
Control
Patients randomized to control will continue routine practice.
No interventions assigned to this group
Interventions
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AI-enabled ECG-based Screening Tool
Primary care clinicians in the intervention group had access to the report, which displayed whether the AI-ECG result was positive or negative.The system will send a message to corresponding physicians if positive finding.
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
20 Years
ALL
No
Sponsors
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National Defense Medical Center, Taiwan
OTHER
Responsible Party
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Chin Lin
Assistant Professor
Locations
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National Defense Medical Center
Taipei, , Taiwan
Countries
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References
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Tsai DJ, Lin C, Liu WT, Lee CC, Chang CH, Lin WY, Liu YL, Chang DW, Hsieh PH, Tsai CS, Chen YH, Hung YJ, Lin CS. Artificial intelligence-assisted diagnosis and prognostication in low ejection fraction using electrocardiograms in inpatient department: a pragmatic randomized controlled trial. BMC Med. 2025 Jun 9;23(1):342. doi: 10.1186/s12916-025-04190-z.
Other Identifiers
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NDMC2021001
Identifier Type: -
Identifier Source: org_study_id
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