Abstract
Type 2 diabetes mellitus (T2DM) is associated with increased skeletal fragility, yet standard clinical assessments often fail to detect diabetes-induced changes in bone quality. Raman spectroscopy (RS), a label-free and non-destructive technique, offers insight into bone composition, and its full spectral profile may reveal changes not captured by traditional compositional metrics. Prior to propagating a crack from a micro-notch to failure, we acquired RS data from human cortical bone samples extracted from fresh-frozen, cadaveric femurs: 60 non-diabetic donors and 60 T2DM donors (equal number of females and males between 50 years and 97 years of age). Eight ML models, including random forest (RF), support vector regression (SVR), ridge regression (RR), partial least squares (PLS), Extreme Gradient Boosting (XGBoost), gradient boosting machine (GBM), Adaptive Boosting (Adaboost), and Stacking, were evaluated for their ability to predict fracture toughness properties from the RS data. Using full-spectrum input, stacking regression yielded the best performance for predicting both crack initiation toughness (R(2) = 0.81, RMSE = 0.08) and the final J-integral or energy required to propagate crack to failure (R(2) = 0.86, RMSE = 0.14). These findings demonstrate that full-spectrum RS combined with ML can capture subtle, functionally relevant alterations in bone composition, enabling prediction of mechanical properties that are otherwise inaccessible. This is the first study to apply RS and ML for fracture toughness prediction in the context of T2DM, demonstrating the potential of spectroscopic approaches to improve assessment of bone quality in metabolic disease.