Understanding bone structure evolution using machine learning

使用机器学习了解骨骼结构进化

• 批准号: DP230101404
• 负责人: Prof Peter Pivonka
• 金额: $ 28.49万
• 依托单位: Queensland University of Technology
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2023
• 资助国家: 澳大利亚
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP230101404
• 关键词: Understanding; bone; structure; evolution; using

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.

骨重建是骨吸收和形成的古老过程，其优化了材料性质并导致了陆生脊椎动物的进化。迄今为止，尚不清楚重塑如何实现骨材料的调整。该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，以解决这个问题。预期成果是开发用于图像分析的多尺度重塑模型和机器学习算法。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显著益处。