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AI could predict strokes before they happen in heart patients

NCT ID NCT07441759

First seen Mar 04, 2026 · Last updated Jun 23, 2026 · Updated 10 times

Summary

This study will test whether an artificial intelligence (AI) tool can better predict stroke risk in 1,000 people aged 65-95 with atrial fibrillation, compared to current methods. The AI analyzes health records and heart data to identify high-risk individuals early. The goal is to prevent strokes and heart attacks by guiding earlier treatment.

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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

Study contacts

  • Contact

    Phone: •••-•••-•••• Email: •••••@•••••

  • Contact

    Phone: •••-•••-•••• Email: •••••@•••••

Locations

  • EAP Tortose est. Servei d'Atencio Primaria i Comunitària. Institut Catala de la Salit

    Tortosa, Tarragona, 43500, Spain

    Contact

    Contact

    Contact Phone: •••-•••-•••• 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.

Active substance

AI-driven risk prediction model (MATHIAS strategy)

What this could lead to

If successful, this could lead to a more accurate way to predict stroke risk in people with atrial fibrillation, potentially preventing strokes and saving healthcare costs.

What could go wrong

This is a model-based evaluation, not a clinical trial testing a treatment. The AI tool may not work as well in real-world settings, and the study hasn't started recruiting yet.

Conditions

The condition(s) this trial relates to.

atrial fibrillation cardiovascular disorder stroke disorder

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.