  Throughout the process of aging, deterioration of bone macro- and
micro-architecture, as well as material decomposition result in a loss of
strength and therefore in an increased likelihood of fractures. To date,
precise contributions of age-related changes in bone (re)modeling and
(de)mineralization dynamics and its effect on the loss of functional integrity
are not completely understood. Here, we present an image-based deep learning
approach to quantitatively describe the dynamic effects of short-term aging and
adaptive response to treatment in proximal mouse tibia and fibula. Our approach
allowed us to perform an end-to-end age prediction based on $\mu$CT images to
determine the dynamic biological process of tissue maturation during a two week
period, therefore permitting a short-term bone aging prediction with $95\%$
accuracy. In a second application, our radiomics analysis reveals that two
weeks of in vivo mechanical loading are associated with an underlying
rejuvenating effect of 5 days. Additionally, by quantitatively analyzing the
learning process, we could, for the first time, identify the localization of
the age-relevant encoded information and demonstrate $89\%$ load-induced
similarity of these locations in the loaded tibia with younger bones. These
data suggest that our method enables identifying a general prognostic phenotype
of a certain bone age as well as a temporal and localized loading-treatment
effect on this apparent bone age. Future translational applications of this
method may provide an improved decision-support method for osteoporosis
treatment at low cost.
