Machine learning helps pinpoint sources of the most common cardiac arrhythmia

Researchers have designed a new ML-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. The team tested their approach on 11 explanted human hearts, all donated posthumously for research purposes. Turned out, that their ML model can indeed efficiently interpret data with an accuracy of up to 81%.