Peer-Reviewed Research

Consumer wearables like Android Wear, Fitbit, and Apple Watch will generate two trillion health measurements this year—far too many for any human doctor to review. To help create the future of preventive medicine, we’re building DeepHeart, a novel deep neural network tested in multiple rigorous clinical studies.

DeepHeart: Semi-Supervised Deep Learning for Cardiovascular Risk Prediction

A major challenge for AI in medicine is that labeled training data is costly, scarce, and closely-guarded. DeepHeart is a semi-supervised deep neural network that accurately predicts cardiovascular risk, but requires 10x less labeled data than conventional deep learning techniques.

DeepHeart: Semi-Supervised Deep Learning for Cardiovascular Risk Prediction

Screening for Atrial Fibrillation

Atrial Fibrillation, the most common abnormal heart rhythm, causes 1 in 4 strokes and frequently goes undiagnosed. In the mRhythm Study, DeepHeart detected atrial fibrillation with 97% accuracy (c-statistic) using optical heart rate sensors, setting the stage for cost-effective, broadly-deployed AF screening.

Screening for Atrial Fibrillation

Screening for Sleep Apnea and Hypertension

More than one billion people worldwide have hypertension and sleep apnea. In this study, DeepHeart detected both conditions with more 82% accuracy using consumer wearables alone.

Screening for Sleep Apnea and Hypertension

High Engagement Mobile Health Research

The first five ResearchKit apps lost 90% of their users in the first 90 days. By taking inspiration from popular consumer apps, we show it’s possible to improve retention by 5x, exceeding even Twitter and Instagram, with high retention across age groups.

High Engagement Mobile Health Research

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