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Longevity startup Gero AI has a mobile API for quantifying health changes – TechCrunch


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 bodily 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.

“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).

Early partners are other longevity-focused companies, AgelessRx and Humanity Inc. But the idea is to get the model widely embedded into fitness apps where it will be able to send a steady stream of longitudinal activity data back to Gero, to further feed its AI’s predictive capabilities and support the broader research mission — where it hopes to progress anti-aging drug discovery, working in partnerships with pharmaceutical companies.

The carrot for the fitness providers to embed the API is to offer their users a fun and potentially valuable feature: A personalized health measurement so they can track positive (or negative) biological changes — helping them quantify the value of whatever fitness service they’re using.

“Every health and wellness provider — maybe even a gym — can put into their app for example… and this thing can rank all their classes in the gym, all their systems in the gym, for their value for different kinds of users,” explains Fedichev.

“We developed these capabilities because we need to understand how aging works in humans, not mice. Once we developed it, we’re using it in our sophisticated genetic research to find genes — we are testing them in the laboratory — but this technology, the measurement of aging from continuous signals like wearable devices, is a good trick on its own. So that’s why we announced this GeroSense project,” he goes on.

“Ageing is this gradual decline of your functional abilities, which is terrible, but you can go to the gym and potentially improve them. But the problem is you’re losing this resilience. This means that when you’re [biologically] stressed, you cannot get back to the norm as quickly as possible. So we report this resilience. So when people start losing this resilience, it means that they’re not robust anymore, and the same level of stress as in their 20s would get them [knocked off] the rails.


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