A new artificial intelligence method developed by investigators at the Smidt Heart Institute based in Cedars-Sinai, Los Angeles, has been shown to detect abnormal heart rhythms associated with atrial fibrillation. that doctors may not pay attention to.
WHY IS IT IMPORTANT?
Researchers at the Smidt Heart Institute say the findings show the potential for artificial intelligence to be used more widely in cardiovascular care.
In a recent study, published in npj Digital Medicine, Cedars-Sinai clinicians show how a deep learning model was developed to analyze images from echocardiograms, including Sound waves show heart rate.
They explained that the researchers trained a program to study more than 100,000 echocardiogram videos from patients with atrial fibrillation. This model distinguishes between echocardiograms that show the heart in sinus rhythm – a normal heart rhythm – and echocardiograms that show the heart in irregular rhythm.
The program could predict which patients in sinus rhythm had experienced – or would develop – atrial fibrillation within 90 days, they said, noting that the image-assessment AI model performed better than with risk estimates based on known risk factors.
“We were able to demonstrate that the deep learning algorithm we developed can be applied to echocardiography to identify patients with an underlying abnormal heart rhythm disorder called atrial fibrillation.”
He added: “Atrial fibrillation can come and go, so it may not show up during a doctor’s appointment. This AI algorithm identifies patients who may have atrial fibrillation even if it doesn’t appear.” appeared during their echocardiographic study.”
BIGGER TREND
The Smidt Heart Institute is the largest cardiothoracic transplant center in California and the third largest in the United States.
According to the CDC, an estimated 12.1 million people in the United States will have atrial fibrillation by 2030. During AFib, the upper chambers of the heart sometimes beat in sync with the lower chambers and sometimes don’t — making doctors It is often difficult for clinicians to detect arrhythmias. In some patients, this condition causes no symptoms at all.
Researchers say a machine learning model trained to analyze echo images could help clinicians detect early subtle changes in the hearts of patients with previously unrecognized arrhythmias. diagnose.
Indeed, AI has long shown promise in early detection of AFib, as evidenced by similar studies at health systems like Geisinger and Mayo Clinic.
ON PROFILE
“We are encouraged that this technology can detect a dangerous condition that is undetectable to the human eye,” said Dr. David Ouyang, a cardiologist and AI researcher at the Smidt Heart Institute. look at the echocardiogram.” “It can be used for patients who are at risk of atrial fibrillation or who are experiencing symptoms related to the condition.”
Dr. Christine M. Albert, chair of the Department of Cardiology at the Smidt Heart Institute, added: “The fact that this program predicts whether patients have active or latent atrial fibrillation could have clinical applications. huge sieve”. “Being able to identify patients with latent atrial fibrillation may allow us to treat them before they experience a serious cardiovascular event.”
Mike Miliard is the executive editor of Healthcare IT News
Email the writer: [email protected]
Healthcare IT News is a HIMSS publication.
