Full Spectrum Raman Spectroscopy Combined With Deep Learning for Prediction of Tensile Mechanical Properties of Human Cortical Bone

ABSTRACT Type 2 diabetes mellitus (T2DM) alters bone quality at the matrix level in ways that current methods do not fully capture. Raman spectroscopy provides molecular information on bone composition and is well suited to probing such alterations. Full‐spectrum Raman data were acquired from cortical bone of fresh‐frozen cadaveric femurs from 60 non‐diabetic and 60 T2DM donors (equal numbers of men and women, aged 50–97 years), followed by uniaxial tensile testing. Machine learning (ML) models (random forest [RF], gradient boosting machine [GBM], adaptive boosting [AdaBoost], stacking) and a one‐dimensional convolutional neural network (1D CNN) were trained on the full spectrum to predict tensile properties. A combined 1D CNN and multilayer perceptron (MLP) model that added clinical variables (age, sex, group) reached the highest performance, with R 2 values from 0.83 to 0.89, while spectral‐only models also performed well. These findings show that full‐spectrum Raman analysis with deep learning (DL) predicts tensile mechanical behavior of human cortical bone. SHapley Additive exPlanations (SHAP) analysis further showed that collagen associated spectral regions, rather than mineral‐related regions, were the primary contributors to the model predictions.