A new study published in The Lancet Digital Health details the development of FaceAge, an AI tool that estimates a person's biological age using facial photographs. Researchers at Mass General Brigham trained the AI on images of adults with cancer, finding that those with younger estimated biological ages tended to have better outcomes after treatment.
The study suggests a strong correlation between the AI's estimated biological age and overall health, potentially impacting treatment decisions. Individuals with cancer whose estimated biological age was older than their chronological age were more likely to die from cancer or other causes. Even individuals without cancer showed a similar correlation; those with older estimated biological ages fared worse.
This facial analysis technology offers a potentially faster and cheaper alternative to existing methods of measuring biological age which rely on blood or saliva samples. Professor William Mair from Harvard suggests the tool's "extraordinary potential" to improve health assessments. While doctors currently visually assess patient health, this AI tool could offer a more data-driven approach.
This research builds upon previous studies, such as a Danish study comparing facial aging and lifespan in twins, that also found links between perceived older appearance and reduced life expectancy. The implications of FaceAge could be significant, potentially improving patient care and offering personalized treatment strategies based on biological age rather than chronological age alone.
It’s no secret that some people appear to age faster than others, especially after enduring stressful periods. But some scientists think a person’s physical appearance could reveal more about them than meets the eye — down to the health of their tissues and cells, a concept known as “biological age.”
In a new study, published Thursday in The Lancet Digital Health, researchers trained artificial intelligence to estimate the biological ages of adults with cancer by analyzing photos of their faces. Study participants with younger estimates tended to fare better after treatment than those deemed older by A.I., researchers at Mass General Brigham found.
The findings suggest that people’s biological age estimates are closely linked to their physical health, which could reflect their ability to survive certain treatments, the authors of the study said. And in the future, facial age analysis may become more useful than age alone in helping doctors make tough calls about their patients’ treatment, they added.
Face-based aging tools have “extraordinary potential” to help doctors quickly and inexpensively estimate how healthy their patients are, compared with existing tests, which use blood or saliva to measure chemical and molecular changes associated with aging, said William Mair, a professor of molecular metabolism at the Harvard T.H. Chan School of Public Health who was not involved in the study. While doctors usually visually estimate how healthy their patients are for their age, a tool like this could draw in much more data to make a better estimate, he added.
FaceAge, the machine learning tool created by researchers at Mass General Brigham, found that study subjects with cancer appeared five years older than their chronological age. The biological age of people without cancer was typically close to their actual age. And those who were categorized as older were more likely to die, either from cancer or other causes.
The researchers are not the first to find a link between facial and biological aging: A study in Denmark found that subjects who looked older than their chronological age tended to die earlier than their twins, and other studies have come to similar conclusions.
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