Sensor data from smartphones and wearables can meaningfully predict an individual’s ‘biological age’ and resilience to stress, according to Gero AI.
The ‘longevity’ startup — which condenses its mission to the pithy goal of “complex hacking diseases and aging with Gero AI” — has developed an AI model to predict morbidity risk using ‘digital biomarkers’ that are based on identifying patterns in step-counter sensor data which tracks mobile users’ physical activity.
A simple measure of ‘steps’ isn’t nuanced enough on its own to predict individual health is the contention. Gero’s AI has been trained on large amounts of biological data to spot patterns that can be linked to morbidity risk. It also measures how quickly a person recovers from physical stress — another biomarker linked to lifespan; i.e., the faster the body recovers from stress, the better the individual’s overall health prognosis.
A research paper Gero has had published in the peer-reviewed biomedical journal Aging explains how it trained deep neural networks to predict morbidity risk from mobile device sensor data — and was able to demonstrate that its biological age acceleration model was comparable to models based on blood test results.
Another paper, due to be published in the journal Nature Communications later this month, will detail its device-derived measurement of biological resilience. The Singapore-based startup, which has research roots in Russia — founded back in 2015 by a Russian scientist with a background in theoretical physics — has raised a total of $5 million in seed funding to date (in two tranches).