The AI system was custom-built by experts at Mass General Brigham, a nonprofit, integrated academic healthcare system in Massachusetts, to estimate biological age and directly predict survival outcomes in patients diagnosed with cancer. It is one of the most comprehensive demonstrations to date of how AI analysis of a selfie could help assess a patient’s health and potentially guide their medical care, and was trained on data from more than 40 million facial photographs across a range of ethnicities and ages. Details of the breakthrough were published in the Lancet Digital Health journal.
The system can estimate a person’s biological age – the age a person looks, rather than their actual, chronological age – and predict the chances of survival for cancer patients from an ordinary photograph of their face, according to the results of a study that examined the model. Doctors used the technology to develop two complementary tools. FAHR-FaceAge estimates a patient’s biological age, and predicts cancer survival.
FAHR-FaceSurvival predicts survival from a facial photograph directly, rather than inferring survival through biological age. Although FAHR-FaceAge and FAHR-FaceSurvival were derived from the same model, they captured different and complementary information about a patient’s health. Each algorithm also relied on distinct parts of the face to make respective predictions of age and mortality risk.
FAHR-FaceAge focuses on the creases that run from the sides of the nose down to the corners of the mouth, the forehead area just above the eye sockets, and the temples. FAHR-FaceSurvival, meanwhile, evaluates the region below the eyes and around the nasal bridge. The patients who looked five or more years younger had a 17% lower risk.
The breakthrough could pave the way for AI facial analysis to assist doctors in making difficult decisions about the best way forward in terms of treatment for their patients. The tool could help better predict the risk of death or complications after major surgery or other treatments for cancer. Dr Raymond Mak, a radiation oncologist at Mass General Brigham, said: “As the population ages, physicians need better ways to estimate physical reserve and frailty before recommending intensive treatments such as radiation, surgery or systemic therapy.
For example, heavy alcohol use, drug use and smoking were linked to looking biologically older in AI analysis of facial photographs.
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