A major failure here is assuming that your hospital has done an MRI on you and has the AI ability.
New AI approach helps detect silent atrial fibrillation in stroke victims
Detecting atrial fibrillation (AF) from brain scans using AI could support future stroke care, according to a recent study published in the Karger journal Cerebrovascular Diseases.
A new study recently published in the journal Cerebrovascular Diseases
shows that artificial intelligence (AI) may help physicians detect a
common, but often hidden, cause of stroke by analyzing brain scans. The
technology could make stroke care faster, more accurate, and more
personalized.
The condition in focus is atrial fibrillation (AF) - a type of irregular
heartbeat that increases stroke risk by five times. Because AF may not
initially present symptoms, it often goes undiagnosed until a stroke has
already occurred. Traditional detection methods, such as prolonged
heart monitoring, can be expensive, invasive, and time-consuming.
This new research from the Melbourne Brain Centre and the University of
Melbourne takes a different approach. By training a machine learning
model on MRI images from patients who have already had strokes, the team
taught the algorithm to recognize patterns linked to AF.
The researchers found that their AI model had "reasonable classification
power" in telling apart strokes caused by AF from those caused by
blocked arteries. In testing, the model achieved a strong performance
score (AUC 0.81), suggesting that AI could become a valuable tool in
helping doctors identify patients who might need further heart testing
or treatment.
As the study notes, "machine learning is gaining greater traction for
clinical decision-making and may help facilitate the detection of
undiagnosed AF when applied to magnetic resonance imaging." Because MRIs
are already a routine part of stroke care, this method doesn't require
extra scans or procedures for patients - making it a low-cost,
non-invasive way to support more targeted care.
The authors of the study emphasize the need for larger follow-up
studies, but the potential is promising: Earlier detection of AF could
lead to more timely treatment and fewer strokes.
"Early detection of atrial fibrillation (AF) is important to offer
patients the best chance of preventing a serious cardioembolic stroke.
However, many patients first present with an acute ischemic stroke for
which the underlying cause of AF is silent because it is asymptomatic
and intermittent," says Craig Anderson, Editor-in-Chief of the journal Cerebrovascular Diseases. "The
work by Sharobeam et al. presents a novel approach to use AI-based
algorithm to inform the diagnosis of AF according to the pattern of
cerebral ischemia on MRI."
Sharobeam, A., et al. (2025). Detecting atrial fibrillation by artificial intelligence enabled neuroimaging examination. Cerebrovascular Diseases. doi.org/10.1159/000543042.
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