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Machine Learning in Atrial Fibrillation

NCT05371405 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 120

Last updated 2025-11-14

No results posted yet for this study

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).

Conditions

Sponsors & Collaborators

Eligibility

Min Age
22 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2020-02-12
Primary Completion
2026-12-31
Completion
2027-12-31

Countries

  • United States

Study Locations

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Entities

Diseases
Companies

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05371405 on ClinicalTrials.gov