  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.
