Summary: Researchers have developed an AI tool called FaceAge that uses facial photos to estimate biological age and predict survival outcomes in cancer patients. In a study involving over 6,000 patients, those with cancer had FaceAges about five years older than their chronological age, and higher FaceAges were linked to poorer survival.
The tool outperformed clinicians in predicting short-term life expectancy for patients receiving palliative radiotherapy, especially when integrated into their decision-making. These findings suggest that facial features could serve as powerful, non-invasive biomarkers for aging and disease, opening new doors in precision medicine.
Key Facts:
- FaceAge AI: Predicts biological age and survival using facial photos.
- Cancer Insight: Patients with cancer appeared ~5 years older than their actual age.
- Clinical Boost: FaceAge improved doctors’ predictions of life expectancy in palliative care.
Source: Mass General
Eyes may be the window to the soul, but a person’s biological age could be reflected in their facial characteristics.
Investigators from Mass General Brigham developed a deep learning algorithm called FaceAge that uses a photo of a person’s face to predict biological age and survival outcomes for patients with cancer.
They found that patients with cancer, on average, had a higher FaceAge than those without and appeared about five years older than their chronological age.
In the cancer patient cohort, older FaceAge was associated with worse survival outcomes, especially in individuals who appeared older than 85, even after adjusting for chronological age, sex, and cancer type.
Estimated survival time at the end of life is difficult to pin down but has important treatment implications in cancer care. The team asked 10 clinicians and researchers to predict short-term life expectancy from 100 photos of patients receiving palliative radiotherapy.
While there was a wide range in their performance, overall, the clinicians’ predictions were only slightly better than a coin flip, even after they were given clinical context, such as the patient’s chronological age and cancer status.
Yet when clinicians were also provided with the patient’s FaceAge information, their predictions improved significantly.
Further research is needed before this technology could be considered for use in a real-world clinical setting. The research team is testing this technology to predict diseases, general health status, and lifespan.
Follow-up studies include expanding this work across different hospitals, looking at patients in different stages of cancer, tracking FaceAge estimates over time, and testing its accuracy against plastic surgery and makeup data sets.
“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,” said co-senior author Ray Mak, MD, a faculty member in the AIM program at Mass General 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.”
Authorship: Additional Mass General Brigham authors include Dennis Bontempi, Osbert Zalay, Danielle S. Bitterman, Fridolin Haugg, Jack M. Qian, Hannah Roberts, Subha Perni, Vasco Prudente, Suraj Pai, Christian Guthier, Tracy Balboni, Laura Warren, Monica Krishan, and Benjamin H. Kann.