To diagnose heart conditions including heart attacks and heart rhythm disturbances, clinicians typically rely on 12-lead electrocardiograms (ECGs) -- complex arrangements of electrodes and wires placed around the chest and limbs to detect the heart's electrical activity.
To diagnose heart conditions including heart attacks and heart rhythm disturbances, clinicians typically rely on 12-lead electrocardiograms -- complex arrangements of electrodes and wires placed around the chest and limbs to detect the heart's electrical activity. But these ECGs require specialized equipment and expertise, and not all clinics have the capability to perform them.
"This opens up the door to patients being able to get really high-quality, time-sensitive clinical data without traveling to somewhere that has a 12-lead ECG," says cardiologist Evan Muse, MD, PhD, the lead of cardiovascular genomics at Scripps Research Translational Institute, assistant professor of Molecular Medicine at Scripps Research and co-senior author of the new paper.
The researchers then took a set of 238 ECGs, with half showing signs of a heart attack. They showed cardiologists either the original 12-lead ECG or an ECG reconstructed by AI using data from the three selected leads. The cardiologists could not identify which was which, and they also correctly identified heart attack indicators 81.4 percent of the time in the AI-generated ECGs -- very close to the 84.6 percent accuracy of original 12-lead ECGs.
"This is an optimal case for AI -- taking a few leads of the electrocardiogram -- to make it remarkably informative, which has big practical implications for patients in the future," says Eric Topol, MD, director and founder of the Scripps Research Translational Institute and executive vice president of Scripps Research.
A new machine learning model uses electrocardiogram readings to diagnose and classify heart attacks faster and more accurately than current approaches, according to a new ...
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