NCT06461468

Hypertrophic Cardiomyopathy Federated Learning Implementation Platform

Detection of Hypertrophic Cardiomyopathy Using Electrocardiograms and Echocardiograms Through Federated Learning

• Status: Enrolling By Invitation
• Phase: —
• Study type: Observational
• Enrollment: 1,000 (estimated)
• Sponsor: American Heart Association · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

HCM FLIP study is a two-phase protocol focusing on the detection of Hypertrophic Cardiomyopathy using Electrocardiograms and Echocardiograms through Federated Learning.

Detailed 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.

Conditions

• Hypertrophic Cardiomyopathy

Timeline

• Start date: 2024-06-05
• Primary completion: 2026-01-01
• Completion: 2026-01-01
• First posted: 2024-06-17
• Last updated: 2024-08-06

Locations

6 sites across 1 country: United States