New AI tool can identify people with abnormal heart rhythms
Atrial fibrillation is an irregular and often very rapid heart rhythm (arrhythmia) that can lead to blood clots in the heart. It increases the risk of stroke, heart failure and other heart-related complications
New York: Researchers have developed an artificial intelligence (AI) based algorithm that can detect an abnormal heart rhythm or atrial fibrillation in people without symptoms.
Atrial fibrillation is an irregular and often very rapid heart rhythm (arrhythmia) that can lead to blood clots in the heart. It increases the risk of stroke, heart failure and other heart-related complications
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The team from the Smidt Heart Institute at Cedars-Sinai Medical Centre found that the algorithm, which identified hidden signals in common medical diagnostic testing, may help doctors better prevent strokes and other cardiovascular complications in people with the most common type of heart rhythm disorder.
“This research allows for better identification of a hidden heart condition and informs the best way to develop algorithms that are equitable and generalisable to all patients,” said David Ouyang, a cardiologist in the Department of Cardiology in the Smidt Heart Institute.
Experts estimate that about 1 in 3 people with atrial fibrillation do not know they have the condition.
In atrial fibrillation, the electrical signals in the heart that regulate the pumping of blood from the upper chambers to the lower chambers are chaotic.
This can cause blood in the upper chambers to pool and form blood clots that can travel to the brain and trigger an ischemic stroke.
To create the algorithm, investigators programmed an artificial intelligence tool to study patterns found in electrocardiogram readings.
An electrocardiogram is a test that monitors electrical signals from the heart. People who undergo this test have electrodes placed on their body that detect the heart’s electrical activity.
The programme was trained to analyse electrocardiogram readings taken between January 1, 1987, and December 31, 2022. The algorithm was trained on almost a million electrocardiograms and it accurately predicted patients would have atrial fibrillation within 31 days.
