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HCM FLIP study is a two-phase protocol focusing on the detection of Hypertrophic Cardiomyopathy using Electrocardiograms and Echocardiograms through Federated Learning.
Full description
HCM FLIP (Hypertrophic Cardiomyopathy Federated Learning Implementation Platform) aim to build and test a model's system impact to detect hypertrophic cardiomyopathy (HCM) by training a machine learning (ML) model with electrocardiograms (ECGs) and echocardiograms (ECHOs). Approximately 10-1000 HCM cases and 30-10,000 age/sex-matched controls per institution, depending on size, will be included in the study. We hypothesize that a federated ML model will discriminate cases of HCM from those without HCM in a real-world setting.
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Inclusion and exclusion criteria
HCM-Labeled Case Inclusion Criteria:
HCM-Labeled Case Exclusion Criteria:
Control Case (Non-HCM) Inclusion Criteria:
Control Case (Non-HCM) Exclusion Criteria
1,000 participants in 2 patient groups
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Central trial contact
Shaina Costello, MPH; Mariel Kaprielian, MPH
Data sourced from clinicaltrials.gov
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