Machine Learning for Fracture Risk Assessment from Simple Radiography

机器学习通过简单射线照相进行骨折风险评估

• 批准号: LP150101052
• 负责人: Prof Ajmal Mian
• 金额: --
• 依托单位: The University of Western Australia
• 依托单位国家: 澳大利亚
• 项目类别: Linkage Projects
• 财政年份: 2016
• 资助国家: 澳大利亚
• 起止时间: 2016-06-16 至 2016-06-17
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/LP150101052
• 关键词: Machine; Learning; Fracture; Risk; Assessment

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.

该项目旨在开发一种新颖、可靠、低成本的系统来检测骨骼健康状况不佳并评估骨折风险，以帮助预防和管理与骨质疏松症相关的骨折。目前，与骨质疏松症相关的骨折每年给我们的卫生系统造成数百万美元的损失，而且随着人口老龄化，成本也在增加。及早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、粗糙的临床和视觉指南，建议进行骨质疏松症筛查。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颖的系统。