New Study Uses Brain Scans to Detect Hidden Heart Risk
                     Can AI Help Prevent the Next Stroke? New Study Uses Brain Scans to 
Detect Hidden Heart Risk
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.”