Fusion of clinical and stochastic finite element data for hip fracture risk prediction.
Fusion of clinical and stochastic finite element data for hip fracture risk prediction.
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DOI：
10.1016/j.jbiomech.2015.09.044
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发表时间：
2015-11-26
影响因子：
2.4
通讯作者：
Chen Z
中科院分区：
文献类型：
--
作者：
Jiang P;Missoum S;Chen Z
髋部骨折每年影响美国超过25万人，全球160万人。随着人口老龄化，开发可靠的骨折风险模型因此至关重要。由于髋部骨折现象的复杂性，仅使用传统的临床数据可能不足以确保准确且强大的髋部骨折预测模型。为了提高风险模型的预测能力，作者建议用有限元模型的计算数据补充临床数据。这两种类型的数据的融合是使用确定性和随机计算数据。在后一种情况下，股骨的载荷和材料特性的不确定性被考虑并通过有限元模型传播。使用妇女健康倡议（WHI）数据集，通过结合临床和有限元数据构建的支持向量机（SVM）风险模型的预测能力进行了评估。该数据集包括常见因素，如年龄和BMD以及从DXA成像获得的几何因素。计算和临床数据的融合系统地导致SVM风险模型的预测能力的增加，如通过AUC度量所测量的。可以得出结论，AUC的最大收益是通过随机方法获得的。这种增益随着问题的维数增加而减小：对于涉及几何因素和重量的9维问题，实现了5.3%的AUC改善，而对于包括几何和常规因素的20维情况，获得了1.3%的增加。
Hip fracture affects more than 250,000 people in the US and 1.6 million worldwide per year. With an aging population, the development of reliable fracture risk models is therefore of prime importance. Due to the complexity of the hip fracture phenomenon, the use of clinical data only, as it is done traditionally, might not be sufficient to ensure an accurate and robust hip fracture prediction model. In order to increase the predictive ability of the risk model, the authors propose to supplement the clinical data with computational data from finite element models. The fusion of the two types of data is performed using deterministic and stochastic computational data. In the latter case, uncertainties in loading and material properties of the femur are accounted for and propagated through the finite element model. The predictive capability of a support vector machine (SVM) risk model constructed by combining clinical and finite element data was assessed using a Women’s Health Initiative (WHI) dataset. The dataset includes common factors such as age and BMD as well as geometric factors obtained from DXA imaging. The fusion of computational and clinical data systematically leads to an increase in predictive ability of the SVM risk model as measured by the AUC metric. It is concluded that the largest gains in AUC are obtained by the stochastic approach. This gain decreases as the dimensionality of the problem increases: a 5.3% AUC improvement was achieved for a 9 dimensional problem involving geometric factors and weight while a 1.3% increase was obtained for a 20 dimensional case including geometric and conventional factors.