Juli Clover, MacRumors:

The heart rate monitors built into the Apple Watch and other wearable devices can detect abnormal heart rhythms with 97 percent accuracy, according to a new study conducted by the team behind the Cardiogram app for Apple Watch in conjunction with researchers at the University of California, San Francisco.

More than 139 million heart rate and step count measurements were collected from 9,750 users of the Cardiogram app who also enrolled in the UC San Francisco Health eHeart Study, with the data used to train DeepHeart, Cardiogram’s deep neural network.

This is a perfect use case for machine learning. Tons of available data, combined with a simple tag. Simple? Read this:

Once trained, DeepHeart was able to read heart rate data collected by wearables, distinguishing between normal heart rhythm and atrial fibrillation with a 97 percent accuracy rate, both when testing UCSF patients with known heart issues and Cardiogram participants.

Basically, DeepHeart plows through a ton of data and gets very good at identifying a heart’s rhythm as either normal or atrial fibrillation. The first phase of machine learning is training, where you hand the model a ton of data, each identified with the proper tag. After all the data is entered, a successful model will be able to identify new data with a high degree of accuracy. 97% is an incredibly good result.

Atrial fibrillation often goes undiagnosed, which is where the Apple Watch and other wearables can help. The Apple Watch won’t replace a traditional EKG, but it can alert people to a problem much earlier than it might otherwise be detected.

This tech will help save lives.