Fracture Prediction by Machine Learning and 3D Finite Element Models from DXA

 描述(申请人提供)：骨质疏松症是一种以骨量丢失和结构恶化为特征的疾病，导致骨折风险增加。目前，骨质疏松症的诊断主要通过双能X线骨密度仪(DXA)测量面骨密度来诊断。然而，大多数骨折发生在女性和男性，他们没有被当前的DXA标准归类为骨质疏松症(T-Score=-2.5)。作为一种二维(2D)技术，DXA不提供有关三维(3D)骨结构、形状和几何的信息，这些信息对骨强度和抗折性有很大贡献。定量计算机断层扫描(QCT)图像的有限元(FE)分析可以提供三维结构和强度测量，但QCT因辐射暴露高和费用高而不能广泛应用于临床。相比之下，DXA随处可得，价格低廉，辐射暴露低。所需要的是一种方法，通过该方法，可以使用DXA图像来生成结合了骨骼结构和几何的3D形状模型。然而，骨折是受其他因素影响的复杂事件，包括年龄、种族、体重指数、跌倒的风险以及既往的病史和骨折史。即使是复杂的骨密度、结构和强度的测量也可能不能准确预测骨折。机器学习是一个新兴的领域，其中的模型是通过从先前的数据中“学习”来创建的。这些模型可以结合各种因素，并用于对新数据的结果进行分类或预测。这项建议的总体假设是，对广泛可用的DXA图像进行先进的分析，结合结构和强度信息，并使用机器学习进行统计建模，以纳入额外的风险因素，将更好地识别出骨质疏松性骨折的高风险患者。这一假设将使用QCT和DXA先前研究的数据进行验证，以生成描述股骨近端形态变化的3D统计形状模型。通过将2D DXA图像与模型对齐，将重建患者特定的3D模型进行定量分析，并与有限元分析结合以估计骨强度。在两个非常大的前瞻性研究中，机器学习模型将被用来结合这些新的测量方法、人口统计学和各种骨折风险因素来预测骨折的发生。这项建议的最终目标是通过应用新的图像处理和统计技术来更准确地预测骨折，增加DXA的诊断效用，DXA是一种安全、非侵入性和广泛可用的技术。

 DESCRIPTION (provided by applicant): Osteoporosis is a disease characterized by loss of bone mass and structural deterioration leading to increased risk of fracture. Currently, osteoporosis is diagnosed by measurement of areal bone mineral density by dual- energy x-ray absorptiometry (DXA). However, the majority of fractures occur in both women and men who are not classified as osteoporotic by current DXA criteria (T-score = -2.5). As a 2-dimensional (2D) technology, DXA does not provide information about 3-dimensional (3D) bone structure, shape and geometry, which substantially contribute to bone strength and resistance to fracture. Finite element (FE) analysis of quantitative computed tomography (QCT) images can provide 3D structure and strength measurements but QCT is impractical for widespread clinical use because of high radiation exposure and expense. In contrast, DXA is widely available, inexpensive and has low radiation exposure. What is needed is a method by which DXA images can be used to generate 3D shape models that incorporate bone structure and geometry. However, fractures are complex events influenced by other factors including age, race, body mass index, risk of falls, and prior medical and fracture history. Even sophisticated measurements of bone density, structure, and strength may not be able to predict fractures accurately. Machine learning is an emerging field in which models are created by "learning" from previous data. These models can incorporate various factors and be used to classify or predict outcomes for new data. The overall hypothesis of this proposal is that advanced analyses of widely available DXA images that incorporate structural and strength information and statistical modeling using machine learning to incorporate additional risk factors will better identify patient at high risk of osteoporotic fracture. This hypothesis will be tested using QCT and DXA data from previous studies to generate 3D statistical shape models that describe variability in proximal femur morphology. By aligning 2D DXA images to the models, patient-specific 3D models will be reconstructed for quantitative analyses and combined with FE analysis to estimate bone strength. Machine learning models will be used to incorporate these novel measurements, demographics, and various risk factors for fracture to predict incident fractures in two very large, prospective studies. The ultimate goal of this proposal is to increase the diagnostic utility of DXA, a safe, non-invasive, and widely available technology, by applying novel image processing and statistical techniques to predict fractures more accurately.