AI model predicts sudden cardiac death risk from routine ECG, study finds
By
Jacek Krywko
Summary
A new study published in Nature describes how researchers led by Ziad Obermeyer at UC Berkeley trained a neural network to predict sudden cardiac death risk from a routine 10-second ECG. The AI model identifies patients at high risk who might benefit from implantable defibrillators, addressing the key challenge of determining who needs these devices. The team used a two-model approach: one to predict risk and a second to explain what the first model was detecting, revealing hidden signals in the heart's electrical activity that human clinicians typically miss. This could help prevent many of the 300,000+ annual sudden cardiac deaths in the U.S.
Source
bskyAI model predicts sudden cardiac death risk from routine ECG, study findsscientificamerican.comKey quotes
· 3 pulledSudden cardiac death kills more than 300,000 people in the U.S. each year, even though implantable defibrillators have been able to stop many lethal arrhythmias for decades.
The main issue today isn't the device that stops a cardiac arrest; it is figuring out who needs one.
In a new Nature study, a team led by Ziad Obermeyer, an associate professor at the University of California, Berkeley, trained a neural network to answer that question from a 10-second electrocardiogram.
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