News | ECG | January 08, 2019

EKG With Artificial Intelligence Reliably Detects Heart Failure Precursor

Pairing electrocardiogram with AI offers widely available, inexpensive way to detect asymptomatic left ventricular dysfunction

EKG With Artificial Intelligence Reliably Detects Heart Failure Precursor

January 8, 2019 — A Mayo Clinic study finds that applying artificial intelligence (AI) to a widely available, inexpensive electrocardiogram (EKG) results in a simple, affordable early indicator of asymptomatic left ventricular dysfunction, a precursor to heart failure. The research team found that the AI/EKG test accuracy compares favorably with other common screening tests, such as mammography for breast cancer. The findings were published in Nature Medicine.1

Asymptomatic left ventricular dysfunction is characterized by the presence of a weak heart pump with a risk of overt heart failure. It affects 7 million Americans, and is associated with reduced quality of life and longevity. But asymptomatic left ventricular dysfunction is treatable when identified.

However, there is no inexpensive, noninvasive, painless screening tool for asymptomatic left ventricular dysfunction available for diagnostic use. The Mayo study reports that the best existing screening test for asymptomatic left ventricular dysfunction is to measure natriuretic peptide levels (BNP). Results of BNP have been disappointing, however, and the test requires blood draws. Left ventricular dysfunction typically is diagnosed with expensive and less accessible imaging tests, such as echocardiograms, or computed tomography (CT) or magnetic resonance imaging (MRI) scans.

“Congestive heart failure afflicts more than 5 million people and consumes more than $30 billion in health care expenditures in the U.S. alone,” said 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 said.

In their study, Mayo Clinic researchers hypothesized that asymptomatic left ventricular dysfunction could be reliably detected in the EKG by a properly trained neural network. 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,” noted Friedman.

For more information: www.nature.com/nm

Reference

1. Attia Z.I., Kapa S., Lopez-Jimenez F., et al. Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nature Medicine, Jan. 7, 2019. https://doi.org/10.1038/s41591-018-0240-2

Related Content

An example of a body composition analysis of an abdominal CT slice with the subcutaneous fat in green, skeletal muscle red and visceral fat in yellow. This was automatically identified and analyzed via a deep learning algorithm to assess the risk for heart attack and stroke in more than 12,000 patients. #RSNA2020 #RSNA20 #RSNA

An example of a body composition analysis of an abdominal CT slice with the subcutaneous fat in green, skeletal muscle red and visceral fat in yellow. This was automatically identified and analyzed via a deep learning algorithm to assess the risk for heart attack and stroke in more than 12,000 patients.

Feature | Artificial Intelligence | December 02, 2020
December 2, 2020 – Automated deep learning analysis of abdominal...
The U.S. Food and Drug Administration (FDA) has cleared AliveCor's Kardia AI V2 next generation of interpretive artificial intelligence (AI)-based personal electrocardiogram (ECG) algorithms.

The U.S. Food and Drug Administration (FDA) has cleared AliveCor's Kardia AI V2 next generation of interpretive artificial intelligence (AI)-based personal electrocardiogram (ECG) algorithms.

News | Artificial Intelligence | November 24, 2020
November 24, 2020 — The U.S.
Dia's LVivo artificial intelligence software can help automate many features of echocardiograms to speed workflow and aid novice users. The software is now integrated into the Konica Minolta Exa PACS.

Dia's LVivo artificial intelligence software can help automate many features of echocardiograms to speed workflow and aid novice users. The software is now integrated into the Konica Minolta Exa PACS. 

News | Artificial Intelligence | November 12, 2020
November 12, 2020 – Konica Minolta Healthcare Americas Inc. and DiA Imaging Analysis Ltd.
The artificial intelligence-driven Caption Guidance software guides point of care ultrasound (POCUS) users to get optimal cardiac ultrasound images. The AI software is an example of a FDA-cleared software that is helping improve imaging, even when used by less experienced users.

The artificial intelligence-driven Caption Guidance software guides point of care ultrasound (POCUS) users to get optimal cardiac ultrasound images. The AI software is an example of a FDA-cleared software that is helping improve imaging, even when used by less experienced users.

Feature | Artificial Intelligence | September 29, 2020 | Joe Fornadel, J.D., and Wes Moran, J.D.
The number of Federal Drug Administration (FDA)-approved AI-based algorithms is significant and has grown at a steady
Selfied might be used with AI to identify patients with heart disease. Getty Images

Getty Images

News | Artificial Intelligence | August 24, 2020
August 24, 2020 — Sending a photo selfie to the doctor could be a cheap and simple way of detecting heart disease usi
aption Health a leader in medical AI technology, has received U.S. Food and Drug Administration (FDA) 510(k) clearance for an updated version of Caption Interpretation, which uses artificial intelligence (AI) to enable clinicians to obtain quick, easy and accurate measurements of cardiac ejection fraction (EF) at the point of care.

Caption Health a leader in medical AI technology, has received U.S. Food and Drug Administration (FDA) 510(k) clearance for an updated version of Caption Interpretation, which uses artificial intelligence (AI) to enable clinicians to obtain quick, easy and accurate measurements of cardiac ejection fraction (EF) at the point of care.

News | Artificial Intelligence | August 19, 2020
August 19, 2020 — Caption Health a leader in medical AI technology, has received U.S.
In February 2020, the U.S. Food and Drug Administration (FDA) cleared artificial intelligence software to assist in the acquisition of cardiac ultrasound images. The Caption Guidance software from Caption Health is an accessory to compatible diagnostic ultrasound systems and uses artificial intelligence to help the user capture images of a patient’s heart that are of acceptable diagnostic quality. It is aimed at point of care ultrasound (POCUS) exams, where users may not be regular sonographers.

In February 2020, the U.S. Food and Drug Administration (FDA) cleared artificial intelligence software to assist in the acquisition of cardiac ultrasound images. The Caption Guidance software from Caption Health is an accessory to compatible diagnostic ultrasound systems and uses artificial intelligence to help the user capture images of a patient’s heart that are of acceptable diagnostic quality. It is aimed at point of care ultrasound (POCUS) exams, where users may not be regular sonographers.

Feature | Artificial Intelligence | August 18, 2020 | Dave Fornell, Editor
The No.