Cedars-Sinai research shows deep learning model could improve AFib detection

Cedars-Sinai research shows deep learning model could improve AFib detection

Photo: Cedars Sinai Trained using more than 100,000 echocardiograms from atrial fibrillation cases, the algorithm has shown its ability to predict which patients could develop irregular heart rhythms within 90 days. A new artificial intelligence approach developed by investigators in Cedars-Sinai's Los Angeles-based Smidt Heart Institute has been shown to detect abnormal heart rhythms associated with atrial fibrillation that might otherwise be unnoticed by physicians. WHY IT MATTERS Researchers at Smidt Heart Institute say the findings point to the potential for artificial intelligence to be used more widely in cardiac care. In a recent study, published in  npj Digital Medicine , Cedars-Sinai clinicians show how the deep learning model was developed to analyze images from echocardiogram imaging, in which sound waves show the heart's rhythm. Researchers trained a program to study more than 100,000 echocardiogram videos from patients with atrial fibrillation, they explain. The model dis…