DALLAS, Nov. 11, 2019 -- Artificial intelligence can examine electrocardiogram (ECG) test results, a common medical test, to pinpoint patients at higher risk of developing a potentially dangerous irregular heartbeat (arrhythmia) or of dying within the next year, according to two preliminary studies to be presented at the American Heart Association's Scientific Sessions 2019 -- November 16-18 in Philadelphia. The Association's Scientific Sessions is an annual, premier global exchange of the latest advances in cardiovascular science for researchers and clinicians.

Researchers used more than 2 million ECG results from more than three decades of archived medical records in Pennsylvania/New Jersey's Geisinger Health System to train deep neural networks -- advanced, multi-layered computational structures. Both studies, from the same group of researchers, are among the first to use artificial intelligence to predict future events from an ECG rather than to detect current health problems, the scientists noted.

"This is exciting and provides more evidence that we are on the verge of a revolution in medicine where computers will be working alongside physicians to improve patient care," said Brandon Fornwalt, M.D., Ph.D., senior author on both studies and associate professor and chair of the Department of Imaging Science and Innovation at Geisinger in Danville, Pennsylvania.

A deep neural network for predicting incident atrial fibrillation directly from 12-lead electrocardiogram traces (Poster Presentation MDP106)

Researchers speculated that a deep learning model could predict irregular heart rhythms, known as atrial fibrillation (AF), before it develops. Atrial fibrillation is associated with higher risk of stroke and heart attack. Focusing on 1.1 million ECGs that did not indicate the presence of AF in more than 237,000 patients, researchers used highly specialized computational hardware to train a deep neural network to analyze 15 segments of data -- 30,000 data points -- for each ECG.

The researchers found that within the top 1% of high-risk patients, as predicted by the neural network, 1 out of every 3 people was diagnosed with AF within a year. The model predictions also demonstrated longer term prognostic significance as the patients predicted to develop AF at 1-year had a 45% higher hazard rate in developing AF over 25-year follow-up than the other patients.

"Currently, there are limited methods to identify which patients will develop AF within the next year, which is why, many times, the first sign of AF is a stroke," said senior author Christopher Haggerty, Ph.D., assistant professor in the Department of Imaging Science and Innovation at Geisinger. "We hope that this model can be used to identify patients with atrial fibrillation very early so they can be treated to prevent stroke."

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