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Apple Watch Can Detect An Abnormal Heart Rhythm With 97 Percent Accuracy, UCSF Study Says

According to a study conducted through heartbeat measurement app Cardiogram and the University of California, San Francisco, the Apple Watch is 97 percent accurate in detecting the most common abnormal heart rhythm when paired with an AI-based algorithm. TechCrunch reports: The study involved 6,158 participants recruited through the Cardiogram app on Apple Watch. Most of the participants in the UCSF Health eHeart study had normal EKG readings. However, 200 of them had been diagnosed with paroxysmal atrial fibrillation (an abnormal heartbeat). Engineers then trained a deep neural network to identify these abnormal heart rhythms from Apple Watch heart rate data. Cardiogram began the study with UCSF in 2016 to discover whether the Apple Watch could detect an oncoming stroke. About a quarter of strokes are caused by an abnormal heart rhythm, according to Cardiogram co-founder and data scientist for UCSF’s eHeart study Brandon Ballinger. Cardiogram tested the deep neural network it had built against 51 in-hospital cardioversions (a procedure that restores the heart’s normal rhythm) and says it achieved a 97 percent accuracy in the neural network’s ability to find irregular heart activity. Additional information available via a Cardiogram blog post.

Read more of this story at Slashdot.

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