This graphic is a illustration of how FaceAge is likely to be calculated from a photograph of a affected person. The affected person on this picture is AI-generated. Credit score: Mass Common Brigham
Eyes often is the window to the soul, however an individual’s organic age could possibly be mirrored of their facial traits. Investigators from Mass Common Brigham developed a deep studying algorithm known as “FaceAge” that makes use of a photograph of an individual’s face to foretell organic age and survival outcomes for sufferers with most cancers.
They discovered that sufferers with most cancers, on common, had a better FaceAge than these with out and appeared about 5 years older than their chronological age.
Older FaceAge predictions have been related to worse total survival outcomes throughout a number of most cancers sorts. Additionally they discovered that FaceAge outperformed clinicians in predicting short-term life expectations of sufferers receiving palliative radiotherapy.
Their outcomes are printed in The Lancet Digital Well being.
“We can use artificial intelligence (AI) to estimate a person’s biological age from face pictures, and our study shows that information can be clinically meaningful,” mentioned co-senior and corresponding writer Hugo Aerts, Ph.D., director of the Synthetic Intelligence in Medication (AIM) program at Mass Common Brigham.
“This work demonstrates that a photo like a simple selfie contains important information that could help to inform clinical decision-making and care plans for patients and clinicians. How old someone looks compared to their chronological age really matters—individuals with FaceAges that are younger than their chronological ages do significantly better after cancer therapy.”
When sufferers stroll into examination rooms, their look could give physicians clues about their total well being and vitality. These intuitive assessments mixed with a affected person’s chronological age, along with many different organic measures, could assist decide the most effective course of therapy.
Nevertheless, like anybody, physicians could have biases about an individual’s age which will affect them, fueling a necessity for extra goal, predictive measures to tell care choices.
With that aim in thoughts, Mass Common Brigham investigators leveraged deep studying and facial recognition applied sciences to coach FaceAge. The software was skilled on 58,851 photographs of presumed wholesome people from public datasets. The group examined the algorithm in a cohort of 6,196 most cancers sufferers from two facilities, utilizing images routinely taken firstly of radiotherapy therapy.
Outcomes confirmed that most cancers sufferers seem considerably older than these with out most cancers, and their FaceAge, on common, was about 5 years older than their chronological age. Within the most cancers affected person cohort, older FaceAge was related to worse survival outcomes, particularly in people who appeared older than 85, even after adjusting for chronological age, intercourse, and most cancers sort.
Estimated survival time on the finish of life is tough to pin down however has essential therapy implications in most cancers care. The group requested 10 clinicians and researchers to foretell short-term life expectancy from 100 photographs of sufferers receiving palliative radiotherapy.
Whereas there was a variety of their efficiency, total, the clinicians’ predictions have been solely barely higher than a coin flip, even after they got scientific context, such because the affected person’s chronological age and most cancers standing. But when clinicians have been additionally supplied with the affected person’s FaceAge info, their predictions improved considerably.
“This opens the door to a whole new realm of biomarker discovery from photographs, and its potential goes far beyond cancer care or predicting age,” mentioned co-senior writer Ray Mak, MD, a college member within the AIM program at Mass Common Brigham.
“As we increasingly think of different chronic diseases as diseases of aging, it becomes even more important to be able to accurately predict an individual’s aging trajectory. I hope we can ultimately use this technology as an early detection system in a variety of applications, within a strong regulatory and ethical framework, to help save lives.”
Extra info:
Bontempi, et al. FaceAge, a deep studying system to estimate organic age from face images to enhance prognostication: a mannequin improvement and validation research, The Lancet Digital Well being (2025). DOI: 10.1016/j.landig.2025.03.002
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AI software makes use of face photographs to estimate organic age and predict most cancers outcomes (2025, Might 8)
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