# Decoding the Rejuvenating Effects of Mechanical Loading on Skeletal Maturation using in Vivo Imaging and Deep Learning

## TL;DR

This study introduces a deep learning and imaging-based method to predict bone aging and assess rejuvenating effects of mechanical loading in mice, offering insights into bone health and potential osteoporosis treatments.

## Contribution

The paper presents a novel end-to-end deep learning approach for short-term bone age prediction and reveals localized rejuvenating effects of mechanical loading in bones.

## Key findings

- Achieved 95% accuracy in short-term bone age prediction from μCT images.
- Discovered that two weeks of mechanical loading have a rejuvenating effect equivalent to 5 days.
- Identified specific bone regions where loading induces age-related changes.

## Abstract

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.