Machine learning helps pinpoint sources of the most common cardiac arrhythmia – Science Codex

Researchers from Skoltech and their US colleagues have designed a new machine learning-based approach for detecting atrial fibrillation drivers, small patches of the heart muscle that are hypothesized to cause this most common type of cardiac arrhythmia. This approach may lead to more efficient targeted medical interventions to treat the condition that is estimated to affect more than 33 million people worldwide, according to the American Heart Association. The recent paper was published in the…

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