Cedars-Sinai Researchers Design AI Tool to Detect Atrial Fibrillation

​A team of investigators from the Smidt Heart Institute at Cedars-Sinai have developed an artificial intelligence (AI) algorithm that detects abnormal heart rhythms in individuals who do not show any symptoms. Atrial fibrillation (AFib) is an irregular and rapid heart rate that can increase the risk of stroke and other heart-related complications. According to the Centers for Disease Control and Prevention, close to 12.1 million people in the United States have AFib. A new study published in the journal npj Digital Medicine shows that the AI program/algorithm can detect abnormal heart rhythms that may go unnoticed during medical check-ups.

The study’s findings suggest AI can analyze images from an echocardiogram, a common imaging test that uses sound waves to capture images of the heart. In atrial fibrillation, electrical signals in the heart can malfunction. This causes blood in the upper chambers to accumulate and may form blood clots or travel to the brain causing an ischemic stroke. To create the algorithm, investigators designed an AI tool to study patterns from electrocardiogram readings. In an electrocardiogram, a medical professional places electrodes on the patient's body to monitor the heart’s electrical activity.

The AI tool analyzed electrocardiogram readings from patients visiting two Veterans Affairs health networks between January 1, 1987, and December 31, 2022. The algorithm, tested on nearly a million electrocardiograms, accurately predicted the likelihood of patients having AFib within 31 days. The AI model applied to patient medical records at Cedars-Sinai accurately predicted AFib cases within 31 days. According to David Ouyang, MD, a cardiologist in the Department of Cardiology at the Smidt Heart Institute at Cedars-Sinai, the research provided an effective way of identifying hidden heart conditions.

Sanjiv Narayan

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