  From birth to death, we all experience surprisingly ubiquitous changes over
time due to aging. If we can predict aging in the digital domain, that is, the
digital twin of the human body, we would be able to detect lesions in their
very early stages, thereby enhancing the quality of life and extending the life
span. We observed that none of the previously developed digital twins of the
adult human body explicitly trained longitudinal conversion rules between
volumetric medical images with deep generative models, potentially resulting in
poor prediction performance of, for example, ventricular volumes. Here, we
establish a new digital twin of an adult human body that adopts longitudinally
acquired head computed tomography (CT) images for training, enabling prediction
of future volumetric head CT images from a single present volumetric head CT
image. We, for the first time, adopt one of the three-dimensional flow-based
deep generative models to realize this sequential three-dimensional digital
twin. We show that our digital twin outperforms the latest methods of
prediction of ventricular volumes in relatively short terms.
