Can AI Help Prevent Future Strokes by Detecting Hidden Heart Risks Through Brain Scans

A groundbreaking study published in the journal Cerebrovascular Diseases explores how artificial intelligence (AI) can assist in identifying hidden heart conditions that increase the risk of stroke. Researchers from the Melbourne Brain Center and the University of Melbourne have developed an innovative approach that leverages machine learning algorithms to analyze MRI brain scans for signs linked to atrial fibrillation (AF), a common but often undiagnosed heart rhythm disorder.

Atrial fibrillation is a major risk factor for stroke, increasing the likelihood by five times. Since AF can be asymptomatic and intermittent, it frequently remains undetected until a person suffers a stroke. Standard detection methods involve extended heart monitoring, which can be invasive and costly. The new AI-based technique offers a non-invasive, cost-effective alternative by analyzing existing MRI scans that are routinely performed during stroke assessments.

The study trained a machine learning model on MRI data from patients who had already experienced strokes. The AI was able to recognize patterns associated with AF, achieving a classification performance with an AUC of 0.81, which indicates high accuracy. This suggests that AI could help clinicians identify patients at risk of undiagnosed AF, prompting earlier and targeted intervention to prevent stroke.

According to the researchers, integrating AI into stroke care could enhance diagnostic precision without requiring additional scans, making it a practical tool for routine clinical use. The potential of this technology lies in its ability to facilitate earlier detection, which is critical because timely treatment of AF can significantly reduce stroke incidence.

However, the authors emphasize that larger follow-up studies are necessary to validate these findings fully. If confirmed, this approach could revolutionize stroke prevention strategies by enabling personalized and proactive healthcare.

Craig Anderson, Editor-in-Chief of Cerebrovascular Diseases, highlighted the significance of these findings, noting that early detection of AF through brain imaging could offer a new avenue for reducing stroke-related morbidity and mortality. As Dr. Anderson stated, "The work by Sharobeam et al. introduces a novel AI-driven method to diagnose atrial fibrillation via cerebral ischemia patterns on MRI, which could greatly enhance stroke prevention efforts."

This research underscores the growing role of AI in medicine, particularly in neurovascular and cardiovascular diagnostics, paving the way for more effective, non-invasive, and accessible stroke prevention techniques. Source: https://medicalxpress.com/news/2025-05-ai-brain-scans-hidden-heart.html