Analyzing facial images using a foundational artificial intelligence (AI) model may help researchers determine both biological aging and disease-related mortality risk, according to study findings published in The Lancet Digital Health.
“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,” said co-corresponding author Raymond H. Mak, MD, a radiation oncologist at Mass General Brigham Cancer Institute and a faculty member in the Artificial Intelligence in Medicine (AIM) program at Mass General Brigham. “Our goal is to make face photograph–based health assessment more quantitative, consistent, and scalable so it can support personalized treatment decisions rather than replace physician judgment.”
Background
Previous research, identified through a systematic search of PubMed and Google Scholar for studies regarding facial image analysis for biological age estimation and survival prediction, has demonstrated the utility of using facial images for chronologic and biological age estimation. The prior research also includes a study from the same team looking at using facial images to estimate biological age to improve prognostication.
All prior research relied on supervised training of models with incomplete or biased data sets, making the findings inconsistently generalizable.
Study Methods
The researchers developed a foundation model, called Foundation Artificial Intelligence for Health Recognition-Face (FAHR-Face), which was trained on more than 40 million facial photographs using masked autoencoders and fine-tuned to estimate biological age (FAHR-FaceAge) and survival prediction (FAHR-FaceSurvival).
For the biological age task, FAHR-FaceAge underwent two-stage, age-balanced fine-tuning on 10 public image data sets. For the survival prediction task, FAHR-FaceSurvival was fine-tuned on 34,363 photographs of patients with cancer. During development, the researchers focused on testing the robustness and independence of the model. The models were also tested on two independent data sets of patients with cancer.
“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 co-senior author Hugo J.W.L. Aerts, PhD, Director of AIM at Mass General Brigham. “This foundation model approach gives us a scalable way to develop multiple facial health biomarkers, rather than training each model from scratch.”
Key Findings
In terms of biological age estimation, FAHR-FaceAge showed the lowest mean absolute error on public data sets compared with benchmark models. Accuracy was maintained across the full human lifespan.
FAHR-FaceAge outperformed FaceAge, from the team's prior research, in terms of survival prognostication.
FAHR-FaceSurvival demonstrated robust prediction of cancer mortality. Patients in the highest-risk quartile showed a significantly increased risk of mortality than patients in the lowest-risk quartile (adjusted hazard ratio [HR] = 3.23; P < .0001).
“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,” Dr. Aerts said. “To our knowledge, this is the first demonstration that an AI model can directly predict cancer survival from a simple photograph.”
Both models showed generalizability in independent validation across age, sex, race, and cancer subgroups. Their predictions were only weakly correlated with each other and were independently associated with survival when analyzed in tandem. They also provided complementary prognostic information for patients with cancer based on distinct facial regions.
Lifestyle factors contributed greatly to signs of facial aging with smoking showing the strongest association with looking biologically older in facial images.
“Another surprising finding was how consistent FAHR-FaceAge was across common real-world photo variations, including pose, lighting, facial expression and makeup,” noted Dr. Mak. “It was also stable across most tested plastic surgery procedures, with only a small shift observed after rhinoplasty.”
The study authors believed that routine quantitative facial analysis could enhance risk stratification for patients with cancer when traditional biomarkers are unavailable.
Going forward, the team is expanding their research through the FaceAge Research Study open to the public.
DISCLOSURES: This study was funded by the E.U. European Research Council. For full disclosures of the study authors, visit thelancet.com.
