Jennifer Hall, Ph.D., the American Heart Association Chief of the Institute for Precision Cardiovascular Medicine, noted 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."
"Being able to understand who is at risk for having irregular heartbeats or atrial fibrillation then helps us understand who may be at risk of also having a stroke and then treating these individuals and preventing both atrial fibrillation and perhaps a stroke down the road," Hall said. "Having these techniques at our fingertips and having more precise techniques to uncover potential atrial fibrillation now or in the future, is absolutely tremendous."
Deep neural networks can predict one-year mortality directly from ECG signal even when clinically interpreted as normal (Oral Presentation 119)
To help identify patients most likely to die of any cause within a year, Geisinger researchers 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 1-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," said Fornwalt, who co-directs Geisinger's Cardiac Imaging Technology Lab with Haggerty. "This could completely alter the way we interpret ECGs in the future."
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, Ph.D. Researchers are now testing whether the predictions can be used to improve health outcomes.
