Machine Learning Acceleration of the DXA2FEM Pipeline for Femoral Bone Strength Prediction

Sammanfattning

Fragility fractures are a major health concern and accurate estimation of bone strength is important for fracture risk assessment. Finite element (FE) models can provide reliable estimates of femoral bone strength, but traditional approaches typically require three-dimensional imaging and computationally intensive model generation. Previous research has enabled reconstruction of subject-specific FE models by only using dual-energy X-ray absorptiometry (DXA) images, but the optimization procedure used to estimate the model parameters can require several hours per patient, which is not feasible in a clinical scenario. The aim of this thesis was to investigate whether machine learning can be used to accelerate these reconstruction pipelines. Two neural network architectures were developed: a baseline convolutional neural network (CNN) and a transfer learning network based on EfficientNetV2B2. The networks were trained using DXA images from the MrOS Sweden cohort to either predict the statistical shape and appearance model (SSAM) parameters used for reconstruction or predict bone strength directly from the images. Artificially generated digitally reconstructed radiographs were also evaluated as synthetic training data. The results show that neural networks can predict SSAM parameters and approximate bone strength estimates with a substantially lower computational cost; potentially saving several hours in computation time. The baseline CNN achieved slightly lower prediction errors than the transfer learning model in most metrics, both predicting reconstruction parameters and bone strength. The most accurate bone strength results were obtained when predicting bone strength directly from DXA images. Although the predicted SSAM parameters likely cannot yet replace the optimization procedure used in existing methods, they could be used to initialize the optimization algorithm. Future work will investigate whether such initialization can reduce convergence time and thereby accelerate the reconstruction process, as well as whether direct bone strength predictions are sufficiently accurate for clinical use.