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Bone degradation, especially for astronauts in microgravity conditions, iscrucial for space exploration missions since the lower applied external forcesaccelerate the diminution in bone stiffness and strength substantially.Although existing computational models help us understand this phenomenon andpossibly restrict its effect in the future, they are time-consuming to simulatethe changes in the bones, not just the bone microstructures, of each individualin detail. In this study, a robust yet fast computational method to predict andvisualize bone degradation has been developed. Our deep-learning method,TransVNet, can take in different 3D voxelized images and predict theirevolution throughout months utilizing a hybrid 3D-CNN-VisionTransformerautoencoder architecture. Because of limited available experimental data andchallenges of obtaining new samples, a digital twin dataset of diverse andinitial bone-like microstructures was generated to train our TransVNet on theevolution of the 3D images through a previously developed degradation model formicrogravity.

Predicting Bone Degradation Using Vision Transformer and Synthetic Cellular Microstructures Dataset