Aging in an individual refers to the
temporal change, mostly decline, in the body's ability to meet physiological demands.
Biological age (BA) is a biomarker of chronological aging, and can be used to stratify populations to predict certain
age related chronic diseases. BA can be predicted from biomedical features such as
brain MRI,
retina or facial images, but the inherent heterogeneity in the aging process limits the usefulness of BA predicted from individual body systems. The methods disclosed herein teach a multi-
modal Transformer-based architecture with cross-attention which was able to combine facial, tongue and
retina images to estimate BA. The model was trained using facial, tongue and
retina images from 11, 223
healthy subjects, and demonstrated that using a fusion of the three image modalities achieved the most accurate BA predictions. The approach was validated on a test
population of 2,840 individuals with six chronic diseases, and obtained
significant difference between chronological age (CA) and BA (AgeDiff) than that of
healthy subjects. AgeDiff has the potential to be utilized as a standalonfe biomarker, or conjunctively alongside other known factors for
risk stratification and progression prediction of chronic diseases. The results therefore highlight the feasibility of using multi-
modal images to estimate and interrogate the aging process.