AI-enabled EKG holds "great promise" as screening tool for asymptomatic left ventricular dysfunction: study

Study data published in Nature Medicine suggest that applying artificial intelligence (AI) to the electrocardiogram (EKG) turns it into a powerful screening tool to identify left ventricular dysfunction in asymptomatic individuals. Senior author Paul Friedman remarked that "the ability to acquire a ubiquitous, easily accessible, inexpensive recording in 10 seconds – the EKG – and to digitally process it with AI to extract new information about previously hidden heart disease holds great promise for saving lives and improving health."

In the study, researchers paired 12-lead EKG and echocardiogram data, including the left ventricular ejection fraction, from 44 959 patients to train a convolutional neural network to identify patients with ventricular dysfunction using EKG data alone.

When tested on an independent set of 52 870 patients, the network model had sensitivity, specificity, and accuracy of 86.3 percent, 85.7 percent and 85.7 percent, respectively. The authors also found that in patients without ventricular dysfunction, those with a positive AI screen had four times the risk of developing future ventricular dysfunction compared to those with a negative screen.

Friedman noted that "the test not only identified asymptomatic disease, but also predicted risk of future disease, presumably by identifying very early, subtle EKG changes that occur before heart muscle weakness."  

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