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Stanford, iRhythm's deep neural network matches cardiologists at arrhythmia classification

MobiHealth News describes that according to a new study published in Nature Medicine, an algorithm trained via a deep neural network has been able to perform on par with board-certified cardiologists at the annotation of 12 different types of heart rhythms.

Researchers from Stanford University and iRhythm collaborated for the study, which detailed an algorithm trained on 91,232 30-second single-lead ECG readings from 53,877 patients, recorded via iRhythm’s Zio monitoring patch, the company’s signature product.

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