New AI tool can predict cancer survival by a single patient selfie, study suggests

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.

“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 cancer patients treated with radiotherapy in the US, the people who, according to FAHR-FaceAge, looked five or more years older than their actual age had a 21% higher risk of dying during the average four-year follow-up than the rest of the group. The patients who looked five or more years younger had a 17% 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 of the study suggest people’s biological age estimates are closely linked to their physical health, which could reflect their ability to survive certain treatments.

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.

“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 judgment.”

Researchers found lifestyle factors were associated with facial ageing. For example, heavy alcohol use, drug use and smoking were linked to looking biologically older in AI analysis of facial photographs.

“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 while single facial photographs could offer an easy, low-cost way to support personalised treatment decisions, more research was needed before the tool could be widely used.

Kaynak: The Guardian · Özgün metin İngilizcedir.