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Decoding biological age from face photographs using deep learning.

Osbert Zalay^1,2,3, Dennis Bontempi^1,2,4,5, Danielle S Bitterman^1,2

¹Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, United States of America.

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|September 25, 2023

Summary

This summary is machine-generated.

A new deep learning system, FaceAge, estimates biological age from facial photos. It found cancer patients appear older, correlating with worse survival, and can aid end-of-life care decisions.

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Area of Science:

Biomedical Engineering
Artificial Intelligence in Medicine
Gerontology

Background:

Human aging rates vary, making appearance a potential indicator of biological age and health.
Current medical assessments of appearance are subjective and lack standardization.
Facial appearance may offer insights into physiological health beyond chronological age.

Purpose of the Study:

To develop and validate FaceAge, a deep learning system for estimating biological age from facial photographs.
To evaluate the clinical utility and prognostic relevance of FaceAge in cancer patients.
To explore the association of FaceAge with molecular senescence mechanisms.

Main Methods:

Trained FaceAge on 58,851 healthy individuals' facial data.
Validated clinical utility on 6,196 cancer patients from US and Dutch institutions.
Performed survival analysis and integrated FaceAge into clinical prediction models for end-of-life care.

Main Results:

Cancer patients, on average, appeared older than their chronological age.
Increased apparent age correlated with worse overall survival in cancer patients.
FaceAge demonstrated independent prognostic performance across various cancer types and stages.

Conclusions:

FaceAge can improve survival predictions for physicians, particularly in palliative care for incurable cancer patients.
The algorithm offers objective, quantitative measures of biological age from facial images, supporting clinical decision-making.
Findings suggest FaceAge's potential applicability beyond cancer, linking visual appearance to molecular senescence.