To reach this conclusion, researchers from Geisinger Health System in Pennsylvania analyzed the results of 1.77 million ECGs and other records from almost 400,000 patients.
The team used this data to compare machine learning-based models that either directly analyzed the raw ECG signals or relied on aggregated human-derived measures (standard ECG features typically recorded by a cardiologist) and commonly diagnosed disease patterns.
The neural network model that directly analyzed the ECG signals was found to be superior for predicting one-year risk of death. Surprisingly, the neural network was able to accurately predict risk of death even in patients deemed by a physician to have a normal ECG.
Three cardiologists separately reviewed the ECGs that had first been read as normal, and they were generally unable to recognize the risk patterns that the neural network detected, researchers said.
“This is the most important finding of this study. This could completely alter the way we interpret ECGs in the future,” said Brandon Fornwalt, chair of the Department of Imaging Science and Innovation at Geisinger in Danville, Pennsylvania.
Another study by the same group of researchers found that AI-based models can analyse ECG test results and pinpoint patients at higher risk of developing a potentially dangerous irregular heartbeat (arrhythmia).
The team used more than two 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.
They found that Artificial intelligence can examine ECG test results, to predict irregular heartbeat and the death risk, according to the two preliminary studies to be presented at the American Heart Association’s Scientific Sessions 2019 in Philadelphia from November 16-18.
While the vast Geisinger database is a key strength of both studies, the findings should be tested at sites outside of Geisinger, the researchers noted.
“Incorporating these models into routine ECG analysis would be simple. However, developing appropriate care plans for patients based on computer predictions would be a bigger challenge,” said lead author Sushravya Raghunath. Both studies are among the first to use AI to predict future events from an ECG rather than to detect current health problems.
“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 Fornwalt. Atrial fibrillation is associated with higher risk of stroke and heart attack.
Jennifer Hall, the American Heart Association Chief of the Institute for Precision Cardiovascular Medicine, said that deep learning is “terrific as another way for us in our field of cardiovascular medicine to be able to help patients and help those understand the risk of stroke.”
“Having these techniques at our fingertips and having more precise techniques to uncover potential atrial fibrillation now or in the future, is absolutely tremendous,” Hall noted.
(Disclaimer: The opinions expressed in this column are that of the writer. The facts and opinions expressed here do not reflect the views of www.xtechalpha.com.)