Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in older and middle-aged adults, where falls and compromised bone quality are predominant factors. We propose a novel staged model that combines advanced imaging and clinical data to improve predictive performance. By using CNNs to extract features from hip DXA images, along with clinical variables, shape measurements, and texture features, our method provides a comprehensive framework for assessing fracture risk. A staged machine learning-based model was developed using two ensemble models: Ensemble 1 (clinical variables only) and Ensemble 2 (clinical variables and DXA imaging features). This staged approach used uncertainty quantification from Ensemble 1 to decide if DXA features are necessary for further prediction. Ensemble 2 exhibited the highest performance, achieving an AUC of 0.9541, an accuracy of 0.9195, a sensitivity of 0.8078, and a specificity of 0.9427. The staged model also performed well, with an AUC of 0.8486, an accuracy of 0.8611, a sensitivity of 0.5578, and a specificity of 0.9249, outperforming Ensemble 1, which had an AUC of 0.5549, an accuracy of 0.7239, a sensitivity of 0.1956, and a specificity of 0.8343. Furthermore, the staged model suggested that 54.49% of patients did not require DXA scanning. It effectively balanced accuracy and specificity, offering a robust solution when DXA data acquisition is not always feasible. Statistical tests confirmed significant differences between the models, highlighting the advantages of the advanced modeling strategies. Our staged approach could identify individuals at risk with a high accuracy but reduce the unnecessary DXA scanning. It has great promise to guide interventions to prevent hip fractures with reduced cost and radiation.

翻译：尽管医疗水平不断进步，髋部骨折仍给个人及医疗系统带来沉重负担。本文聚焦于中老年人群的髋部骨折风险预测，其中跌倒与骨质量下降是主要影响因素。我们提出一种新颖的分阶段模型，通过结合先进影像学与临床数据来提升预测性能。该方法利用CNN从髋部DXA图像中提取特征，并结合临床变量、形态学测量与纹理特征，构建了评估骨折风险的综合框架。我们采用两个集成模型开发了基于机器学习的分阶段模型：集成模型1（仅含临床变量）与集成模型2（临床变量与DXA影像特征）。该分阶段方法利用集成模型1的不确定性量化结果，判断是否需要DXA特征进行进一步预测。集成模型2表现出最优性能，其AUC达0.9541，准确率0.9195，灵敏度0.8078，特异度0.9427。分阶段模型同样表现良好，其AUC为0.8486，准确率0.8611，灵敏度0.5578，特异度0.9249，显著优于AUC仅0.5549、准确率0.7239、灵敏度0.1956、特异度0.8343的集成模型1。此外，分阶段模型提示54.49%的患者无需进行DXA扫描。该模型在准确率与特异度间取得良好平衡，为DXA数据获取受限的场景提供了稳健解决方案。统计检验证实了模型间的显著差异，凸显了先进建模策略的优势。我们的分阶段方法既能以高精度识别高危个体，又可减少不必要的DXA扫描，在降低成本和辐射暴露的同时，为髋部骨折预防干预提供了极具前景的指导方案。