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RECRUITING
NCT05371405
Machine Learning in Atrial Fibrillation
Sponsor: Stanford University
View on ClinicalTrials.gov
Summary
Atrial fibrillation is a serious public health issue that affects over 5 million Americans (Miyazaka, Circulation 2006) in whom it may cause skipped beats, dizziness, stroke and even death. Therapy for AF is currently suboptimal, in part because AF represents several disease states of which few have been delineated or used to successfully guide management. This study seeks to clarify this delineation of AF types using machine learning (ML).
Key Details
Gender
All
Age Range
22 Years - 80 Years
Study Type
OBSERVATIONAL
Enrollment
120
Start Date
2020-02-12
Completion Date
2027-12
Last Updated
2025-11-14
Healthy Volunteers
No
Conditions
Locations (1)
Stanford University
Stanford, California, United States