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).
Backers come from both the biotech and the AI fields, per co-founder Peter Fedichev. Its investors include Belarus-based AI-focused early-stage fund Bulba Ventures (Yury Melnichek). On the pharma side, it has backing from some (unnamed) private individuals with links to the Russian drug development firm Valenta. (The pharma company itself is not an investor).
Fenichel is a theoretical physicist by training who, after his Ph.D. and some ten years in academia, moved into biotech to work on molecular modeling and machine learning for drug discovery — where he got interested in the problem of aging and decided to start the company.
As well as conducting its own biological research into longevity (studying mice and nematodes), it’s focused on developing an AI model for predicting the biological age and resilience to the stress of humans — via sensor data captured by mobile devices.
“Health, of course, is much more than one number,” emphasizes Fedichev. “We should not have illusions about that. But if you are going to condense human health to one number, then, for many people, biological age is the best number. It tells you — essentially — how toxic is your lifestyle… The more biological age you have relative to your chronological age years — that’s called biological acceleration — the more are your chances to get a chronic disease, to get seasonal infectious diseases or also develop complications from those seasonal diseases.”
Gero has recently launched a (paid, for now) API, called GeroSense, that’s aimed at health and fitness apps so they can tap up its AI modeling to offer their users an individual assessment of biological age and resilience (aka recovery rate from stress back to that individual’s baseline).