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

 描述（由申请人提供）：骨质疏松症是一种以骨量丢失和结构退化为特征的疾病，导致骨折风险增加。目前，骨质疏松症通过双能X射线吸收测定法（DXA）测量面积骨矿物质密度来诊断。然而，大多数骨折发生在女性和男性中，根据目前的DXA标准（T评分= -2.5），这些骨折未被归类为椎体骨折。作为一种二维（2D）技术，DXA不提供有关三维（3D）骨结构、形状和几何形状的信息，这些信息对骨强度和抗骨折性有很大贡献。定量计算机断层扫描（QCT）图像的有限元（FE）分析可以提供3D结构和强度测量，但由于高辐射暴露和费用，QCT对于广泛的临床使用是不切实际的。相比之下，DXA是广泛可用的，廉价的，并且具有低辐射暴露。所需要的是一种方法，通过该方法，DXA图像可以用于生成结合骨结构和几何形状的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、福尔斯风险以及既往病史和骨折史。即使对骨密度、结构和强度的复杂测量也可能无法准确预测骨折。机器学习是一个新兴的领域，其中模型是通过从以前的数据中“学习”来创建的。这些模型可以包含各种因素，并用于对新数据进行分类或预测结果。该提案的总体假设是，对广泛可用的DXA图像进行高级分析，这些图像包含结构和强度信息，并使用机器学习进行统计建模，以纳入其他风险因素，从而更好地识别具有高风险的骨质疏松性骨折患者。将使用先前研究的QCT和DXA数据对该假设进行检验，以生成描述股骨近端形态变异性的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模型以进行定量分析，并结合FE分析来估计骨强度。机器学习模型将用于整合这些新的测量方法，人口统计学和骨折的各种风险因素，以预测两项非常大的前瞻性研究中的骨折事件。该提案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，从而提高DXA的诊断实用性，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.