AI could predict which heart ablations work best
NCT ID NCT05371405
First seen Jun 24, 2026 · Last updated Jun 24, 2026
Summary
This Stanford study is testing whether machine learning can help predict which patients with atrial fibrillation will benefit from a heart ablation procedure. Researchers will collect data from 120 people undergoing ablation and use it to train an algorithm that forecasts success. The goal is to personalize treatment and identify the best targets for ablation.
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This is a summary of
the original study
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Summaries may miss details or leave out important information. Before applying or accepting participation, make sure you have read and understood the full study. Curemydisease.com takes no responsibility whatsoever for anything missed, misunderstood, or acted upon as a result of our summary — we know it does not capture everything.
This is a summary of the original study . Summaries may miss details or leave out important information. Before applying or accepting participation, make sure you have read and understood the full study. Curemydisease.com takes no responsibility whatsoever for anything missed, misunderstood, or acted upon as a result of our summary — we know it does not capture everything.
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Contacts and locations
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Study contacts
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Contact
Email: •••••@•••••
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Contact
Phone: •••-•••-•••• Email: •••••@•••••
Locations
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Stanford University
RECRUITINGStanford, California, 94305, United States
Contact Email: •••••@•••••
Contact Phone: •••-•••-•••• Email: •••••@•••••
What this could mean
Our plain-language read of the trial. This is informational only — not medical advice or a prediction.
What this could lead to
If successful, this could lead to more personalized and effective ablation procedures for atrial fibrillation patients.
What could go wrong
This is an early-stage observational study focused on developing a prediction tool, not testing a new treatment. The algorithm may not improve outcomes in practice.
Conditions
The condition(s) this trial relates to.
As listed by the trial registrant
The condition terms exactly as the trial's registrant entered them.