Data from wearables helped teach an AI to spot signs of diabetes
In a new study conducted with the UCSF Department of Medicine, a neural network developed by a startup called Cardiogram was able to detect diabetes with nearly 85 percent accuracy, just by looking at people's heart beats over time. And the kicker? As always, the study didn't require any fancy medical hardware — just Apple Watches, Fitbits, Android Wear devices, and other wearables with heart rate sensors. "It's the hardest thing I've ever worked on, but the most rewarding," Cardiogram co-founder Brandon Ballinger told Engadget. (Coming from someone who helped overhaul Healthcare.gov , that's saying something.) Cardiogram has conducted similar studies in the past, like when it trained that neural network — DeepHeart — to search for telltale signs of strokes in a pool of roughly 6,000 users. This undertaking required even more work. For the new study, which also attempted to spot high cholesterol, sleep apnea and hypertension in that sea of heartbeats...