AI deep learning model helping cardiologists detect AFib
WASHINGTON DC: Artificial intelligence technology based on a deep learning model could help cardiologists predict irregular heart rhythms, known as atrial fibrillation, before it develops.
That’s the conclusion drawn from two studies to be presented at the American Heart Association Scientific Sessions 2019 and conducted by Geisinger researchers.
A team of scientists trained a neural network to evaluate electrocardiograms to predict which patients were likely to develop an irregular heartbeat, using the AI model to analyze the results of 1.77 million ECGs and other records from almost 400,000 patients.
Researchers trained deep neural networks using ECG results from across 30 years of archived medical records in Pennsylvania and New Jersey’s Geisinger Health System, finding the AI was able to provide longer-term prognostication and more accurately identify at-risk patients.
The model was also able to predict which patients would develop an irregular heartbeat, even when doctors interpreted the test results as normal, by analyzing 15 segments of data comprised of more than 30,000 data points for each ECG.
In what was called the most important finding of this study, the neural network was able to accurately predict risk of death in patients, even when independent cardiologists were unable to recognize those same risk patterns.
