1st automated ECG interpretation pipeline could bolster care in underserved communities

Researchers—led by Rahul Deo, MD, PhD, of the division of cardiovascular medicine at Brigham and Women’s Hospital in Boston—have developed the first pipeline for automated echocardiogram interpretation, according to an article published Sept. 21 by Cardiovascular Business (CVB). 

The automated imaging technology could decrease healthcare costs while helping to expand care in underserved and remote populations, as detailed in the study published online Sept. 17 in Circulation. 

Deo and colleagues used data from 14,035 cardiograms equivalent to a decade’s worth of data to develop their pipeline. Machine learning was also used to train and evaluate convolutional neural network models to identify 23 viewpoints and segmentation of cardiac chambers, which performed with 96 percent accuracy.  

“An automated method to interpret echocardiograms could help democratize echocardiography, shifting evaluation of the heart to the primary care setting and rural areas,” Deo and colleagues wrote. “In addition to clinical use, such a method could also facilitate research and discovery by standardizing and accelerating analysis of the millions of echocardiograms archived within our medical systems.” 

See Cardiovascular Business’ entire article below.