The Lancet. Digital health · 2027
Foundation artificial intelligence models for health recognition by use of face photographs (FAHR-Face): a model development and validation study.
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Haugg Fridolin, Lee Grace, He John, Nürnberg Leonard, Bontempi Dennis, Bitterman Danielle S, Catalano Paul, Prudente Vasco, Glubokov Dmitrii, Warrington Andrew, Pai Suraj, De Ruysscher Dirk, Guthier Christian, Kann Benjamin H, Gladyshev Vadim N, Aerts Hugo Jwl, Mak Raymond H
Аннотация
BACKGROUND: Facial appearance offers a non-invasive window into health. We built Foundation Artificial Intelligence for Health Recognition-Face (FAHR-Face), a foundation model trained on more than 40 million facial photographs and fine-tuned it for two distinct tasks: biological age estimation (FAHR-FaceAge) and survival prediction (FAHR-FaceSurvival).
METHODS: FAHR-FaceAge underwent two-stage, age-balanced fine-tuning on ten public image datasets; FAHR-FaceSurvival was fine-tuned on 34 363 photographs of patients with cancer. Model robustness (cosmetic surgery, make-up, pose, and lighting) and independence (attention mapping) were tested. Both models were clinically tested in two independent cancer patient datasets with survival analysed by multivariate Cox models and adjusted for clinical prognostic factors.
FINDINGS: For age estimation, FAHR-FaceAge had the lowest mean absolute error of 5·1 years on public datasets, outperforming benchmark models and maintaining accuracy across the full human lifespan. In patients with cancer, FAHR-FaceAge outperformed a previous age estimation model in survival prognostication. FAHR-FaceSurvival showed robust prediction of mortality, and the highest-risk quartile had more than triple the mortality of the lowest (adjusted hazard ratio 3·23; p<0·0001). These findings were validated in the independent cohort and both models showed generalisability across age, sex, race, and cancer subgroups. The two algorithms provided distinct, complementary prognostic information; attention mapping revealed that each model relied on distinct facial regions. The combination of FAHR-FaceAge and FAHR-FaceSurvival improved prognostic accuracy.
INTERPRETATION: A single foundation model can generate inexpensive, scalable facial biomarkers that capture both biological ageing and disease-related mortality risk. The foundation model enabled effective training by use of relatively small clinical datasets. Routine quantitative facial analysis could enhance risk stratification when traditional biomarkers are unavailable.
FUNDING: EU European Research Council.
METHODS: FAHR-FaceAge underwent two-stage, age-balanced fine-tuning on ten public image datasets; FAHR-FaceSurvival was fine-tuned on 34 363 photographs of patients with cancer. Model robustness (cosmetic surgery, make-up, pose, and lighting) and independence (attention mapping) were tested. Both models were clinically tested in two independent cancer patient datasets with survival analysed by multivariate Cox models and adjusted for clinical prognostic factors.
FINDINGS: For age estimation, FAHR-FaceAge had the lowest mean absolute error of 5·1 years on public datasets, outperforming benchmark models and maintaining accuracy across the full human lifespan. In patients with cancer, FAHR-FaceAge outperformed a previous age estimation model in survival prognostication. FAHR-FaceSurvival showed robust prediction of mortality, and the highest-risk quartile had more than triple the mortality of the lowest (adjusted hazard ratio 3·23; p<0·0001). These findings were validated in the independent cohort and both models showed generalisability across age, sex, race, and cancer subgroups. The two algorithms provided distinct, complementary prognostic information; attention mapping revealed that each model relied on distinct facial regions. The combination of FAHR-FaceAge and FAHR-FaceSurvival improved prognostic accuracy.
INTERPRETATION: A single foundation model can generate inexpensive, scalable facial biomarkers that capture both biological ageing and disease-related mortality risk. The foundation model enabled effective training by use of relatively small clinical datasets. Routine quantitative facial analysis could enhance risk stratification when traditional biomarkers are unavailable.
FUNDING: EU European Research Council.