Doctors have developed an artificial intelligence tool that can predict a cancer patient’s chances of survival from a single photograph of their face, The Guardian reports.
Experts at Mass General Brigham, a nonprofit academic healthcare system in Massachusetts, built the system to estimate biological age and directly predict survival outcomes in patients with cancer.
Details were published in the Lancet Digital Health journal.
The British newspaper calls it one of the most comprehensive demonstrations yet of how AI analysis of a selfie could help assess a patient’s health and potentially guide care.
The system was trained on more than 40 million facial photographs.
It can estimate biological age, the age a person looks rather than their chronological age, and predict cancer patients’ survival chances from an ordinary face photograph, the study found.
The doctors used the technology to develop two complementary tools.
FAHR-FaceAge estimates biological age and predicts cancer survival, while FAHR-FaceSurvival predicts survival directly rather than inferring it through biological age.
Though derived from the same model, the two captured different and complementary information about a patient’s health.
Each relied on distinct parts of the face: FAHR-FaceAge focuses on the creases from the sides of the nose to the corners of the mouth, the forehead just above the eye sockets and the temples, while FAHR-FaceSurvival evaluates the region below the eyes and around the nasal bridge.
“FAHR-FaceSurvival advances the field of facial health recognition AI a step further by learning survival risk directly from facial photographs of patients with cancer,” said Hugo Aerts, a co-senior author of the study.
“To our knowledge, this is the first demonstration that an AI model can directly predict cancer survival from a simple photograph.”
Among US cancer patients treated with radiotherapy, those who looked five or more years older than their actual age, according to FAHR-FaceAge, had a 21 percent higher risk of dying during the average four-year follow-up than the rest of the group.
Those who looked five or more years younger had a 17 percent lower risk.
“The key technical advance is that FAHR-Face learns a general representation of the face from more than 40 million images, which can then be adapted to distinct health outcomes such as biological age and survival,” said Aerts.
“This foundation-model approach gives us a scalable way to develop multiple facial health biomarkers rather than training each model from scratch.”
The results suggest biological age estimates are closely linked to physical health, which could reflect a person’s ability to survive certain treatments, as per The Guardian.
It says the tool could help doctors make difficult treatment decisions and predict the risk of death or complications after major surgery or other cancer treatments.
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.
“Our goal is to make face photograph-based health assessment more quantitative, consistent and scalable so it can support personalised treatment decisions rather than replace physician judgement.”
Heavy alcohol use, drug use and smoking were linked to looking biologically older in the AI analysis, the researchers found.
“Another surprising finding was how consistent FAHR-FaceAge was across common real-world photo variations, including pose, lighting, facial expression and makeup,” said Mak. “It was also stable across most tested plastic surgery procedures, with only a small shift observed after rhinoplasty.”
Doctors cautioned that more research is needed before the tool can be widely used, the British newspaper reports.