  Bone degradation, especially for astronauts in microgravity conditions, is
crucial for space exploration missions since the lower applied external forces
accelerate the diminution in bone stiffness and strength substantially.
Although existing computational models help us understand this phenomenon and
possibly restrict its effect in the future, they are time-consuming to simulate
the changes in the bones, not just the bone microstructures, of each individual
in detail. In this study, a robust yet fast computational method to predict and
visualize bone degradation has been developed. Our deep-learning method,
TransVNet, can take in different 3D voxelized images and predict their
evolution throughout months utilizing a hybrid 3D-CNN-VisionTransformer
autoencoder architecture. Because of limited available experimental data and
challenges of obtaining new samples, a digital twin dataset of diverse and
initial bone-like microstructures was generated to train our TransVNet on the
evolution of the 3D images through a previously developed degradation model for
microgravity.
