‘Deep aging clocks’ show why you’re only as old as you feel

Researchers have demonstrated two deep learning tools aimed at uncovering the psychology of aging. One tool predicts actual chronological age; the other, a person’s subjective perception of the rate at which he or she is aging.

The researchers, led by AI developers at Hong Kong-based Deep Longevity Inc., also show both tools can predict all-cause mortality risk.

They present their work in a paper published in Aging.

Deep Longevity founder Alex Zhavoronkov, PhD, and colleagues used a deep neural network to classify biomarkers of aging as revealed by behaviors described in responses to biosocial and psychosocial questionnaires.

Their work falls within the field of “aging clock” development, which seeks to understand the human aging process based on quantifiable biomarkers.  

For this reason, the researchers refer to their two new AI tools as “deep aging clocks.”

In a news release sent by the company, Zhavoronkov says the demonstration marks the first time that AI has shown it can “predict human psychological and subjective age and help identify the possible interventions that can be applied in order to help people feel and behave younger.”

Dave Pearson

Dave P. has worked in journalism, marketing and public relations for more than 30 years, frequently concentrating on hospitals, healthcare technology and Catholic communications. He has also specialized in fundraising communications, ghostwriting for CEOs of local, national and global charities, nonprofits and foundations.

Around the web

Compensation for heart specialists continues to climb. What does this say about cardiology as a whole? Could private equity's rising influence bring about change? We spoke to MedAxiom CEO Jerry Blackwell, MD, MBA, a veteran cardiologist himself, to learn more.

The American College of Cardiology has shared its perspective on new CMS payment policies, highlighting revenue concerns while providing key details for cardiologists and other cardiology professionals. 

As debate simmers over how best to regulate AI, experts continue to offer guidance on where to start, how to proceed and what to emphasize. A new resource models its recommendations on what its authors call the “SETO Loop.”