AI/EKG test resulted in a simple, affordable early indicator of asymptomatic left ventricular dysfunction, which is a precursor to heart failure.

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Mayo Clinic researchers hypothesized that asymptomatic left ventricular dysfunction could be reliably detected in the EKG by a properly trained neural network.
"Congestive heart failure afflicts more than 5 million people and consumes more than $30 billion in health care expenditures in the U.S. alone," says Paul Friedman, M.D., senior author and chair of the Midwest Department of Cardiovascular Medicine at Mayo Clinic. "The ability to acquire an 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," he says.
Using Mayo Clinic stored digital data, 625,326 paired EKG and transthoracic echocardiograms were screened to identify the population to be studied for analysis. To test their hypothesis, researchers created, trained, validated and then tested a neural network.
The study concluded that AI applied to a standard EKG reliably detects asymptomatic left ventricular dysfunction. The accuracy of the AI/EKG test compares favorably with other common screening tests, such as prostate-specific antigen for prostate cancer, mammography for breast cancer and cervical cytology for cervical cancer.
In addition, in patients without ventricular dysfunction, those with a positive AI screen were at four times the risk of developing future ventricular dysfunction, compared with those with a negative screen. "In other words, 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," notes Dr. Friedman.
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