A Hybrid XFEM-ML Framework for High-Fidelity, Rapid Fracture Prediction in Cortical Bone: From Microstructure to Clinical Translation

This paper proposes a hybrid computational model, which combines the eXtended Finite Element Method (XFEM) with machine learning (ML) to forecast the fracture behavior of cortical bone while maintaining microstructural fidelity. It has created a parametric dataset of about 450 three-dimensional XFEM models of single-edge notched bend (SENB) specimens, including important μ-structure features (e.g., osteon orientation, cement line properties, interfacial connectivity, etc.). Based on these simulations, 47 quantitative descriptors were obtained, and these were used to model supervised ML models, namely, Random Forest and Artificial Neural Networks, to estimate fracture load. The Random Forest model demonstrated exceptional predictive performance (R2 = 0.952, MAE = 6.1 N, MAPE = 1.9%, Pearson r = 0.980), significantly outperforming the ANN model (R2 = 0.831, MAE = 11.8 N, MAPE = 3.7%), The Random Forest model demonstrated strong predictive performance (R2 = 0.95, MAE = 6.1 N, MAPE = 1.9%), while reducing computational time by nearly 300-fold and memory requirements by over 20-fold compared to full XFEM analyses. For fracture resistance, feature importanceanalysis indicated the most salient features were osteon orientation, cement line strength, and pore topology. The methodology was also robust, as further led to by sensitivity analyses and uncertainty quantification. The hybrid method provides microstructure-based predictions of fracture that are very automated and precise and result in a significant decrease in computational cost, hence allowing a scalable route to clinical translation to characterize bone integrity.