Identifying patients with high risk of hip fracture is a great challenge in osteoporosis clinical assessment. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold standard in osteoporosis clinical assessment. However, its classification accuracy is only around 65%. In order to improve this accuracy, this paper proposes the use of Machine Learning (ML) models trained with data from a biomechanical model that simulates a sideways-fall. Machine Learning (ML) models are models able to learn and to make predictions from data. During a training process, ML models learn a function that maps inputs and outputs without previous knowledge of the problem. The main advantage of ML models is that once the mapping function is constructed, they can make predictions for complex biomechanical behaviours in real time. However, despite the increasing popularity of Machine Learning (ML) models and their wide application to many fields of medicine, their use as hip fracture predictors is still limited. This paper proposes the use of ML models to assess and predict hip fracture risk. Clinical, geometric, and biomechanical variables from the finite element simulation of a side fall are used as independent variables to train the models. Among the different tested models, Random Forest stands out, showing its capability to outperform BMD-DXA, achieving an accuracy over 87%, with specificity over 92% and sensitivity over 83%.

中文翻译：

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基于机器学习的髋部骨折风险评估

在骨质疏松症的临床评估中，识别出髋部骨折高风险患者是一项巨大的挑战。通过双能X线骨密度仪（DXA）测量的骨矿物质密度（BMD）是当前骨质疏松症临床评估的金标准。但是，其分类精度仅为65％左右。为了提高这种准确性，本文提出了使用机器学习（ML）模型进行训练的模型，该模型由来自模拟侧向下落的生物力学模型的数据训练而成。机器学习（ML）模型是能够学习数据并根据数据做出预测的模型。在训练过程中，机器学习模型学习一种无需事先了解问题即可映射输入和输出的函数。ML模型的主要优点是，一旦构建了映射函数，它们就可以实时预测复杂的生物力学行为。但是，尽管机器学习（ML）模型越来越受欢迎，并且在许多医学领域中得到了广泛应用，但它们作为髋部骨折预测指标的应用仍然受到限制。本文提出使用ML模型来评估和预测髋部骨折的风险。来自侧面跌落的有限元模拟的临床，几何和生物力学变量被用作自变量来训练模型。在不同的测试模型中，Random Forest表现出色，表现出优于BMD-DXA的能力，其准确度超过87％，特异性超过92％，灵敏度超过83％。本文提出使用ML模型来评估和预测髋部骨折的风险。来自侧面跌落的有限元模拟的临床，几何和生物力学变量被用作独立变量来训练模型。在不同的测试模型中，Random Forest表现出色，表现出优于BMD-DXA的能力，其准确度超过87％，特异性超过92％，灵敏度超过83％。本文提出使用ML模型来评估和预测髋部骨折的风险。来自侧面跌落的有限元模拟的临床，几何和生物力学变量被用作自变量来训练模型。在不同的测试模型中，Random Forest表现出色，表现出优于BMD-DXA的能力，其准确度超过87％，特异性超过92％，灵敏度超过83％。