Machine Learning for Fracture Risk Assessment from Simple Radiography

该项目旨在开发一种新颖、可靠、低成本的系统，以检测骨骼健康状况不良并评估骨折风险，以帮助预防和管理骨关节炎相关骨折。目前，与骨质疏松症有关的骨折每年使我们的卫生系统损失数百万美元，而且随着人口老龄化，费用正在增加。早期发现骨骼健康状况不佳将提高预防措施的有效性并减轻这一负担。目前的方法包括不可靠的，粗糙的临床和视觉指南，建议骨质疏松症筛查。该项目计划通过将机器学习算法应用于通常用于诊断其他疾病的放射学数据来开发一种新型系统。

This project aims to develop a novel, reliable, low-cost system to detect poor bone health and assess fracture risk to help to prevent and manage osteoporosis-related fractures. Currently, osteoporosis-related fractures cost our health system millions of dollars annually and costs are increasing with our ageing population. Early detection of poor bone health will improve the effectiveness of preventive measures and ease this burden. Current methods include unreliable, crude clinical and visual guides that suggest osteoporosis screening. The project plans to develop a novel system by applying machine learning algorithms to radiology data which is commonly captured for diagnosing other conditions.