Understanding bone structure evolution using machine learning

骨重塑是骨骼吸收和形成的古老过程，它优化了材料特性，并导致了陆生脊椎动物的进化。到目前为止，还不清楚骨重塑如何实现骨材料的调节。本提案旨在开发一种基于机器学习的方法，将计算建模和成像联系起来解决这一问题。预期的结果是用于图像分析的多尺度重塑模型和机器学习算法的发展。这种方法将有助于在重塑和骨材料优化之间建立结构-功能联系，最终为骨组织工程、骨折愈合和改善骨质疏松症治疗提供重大益处。

Bone remodeling is the ancient process of bone resorption and formation that optimises material properties and has led to evolution of terrestrial vertebrates. To date it is not understood how remodeling achieves tuning of bone material. This proposal aims to develop a machine learning based approach, linking computational modeling and imaging to address this problem. Intended outcomes are development of a multiscale model of remodeling and machine learning algorithms for image analysis. This approach will help establish a structural-functional link between remodeling and bone material optimisation which ultimately provides significant benefits for bone tissue engineering, fracture healing and improved therapies for osteoporosis.