Fig. 2.

Workflow of the two-Stage cortical bone analysis using HRpQCT. A. The process initiates with the preprocessing of axial HRpQCT scans, followed by segmentation of non-CKD and CKD cortical bones using a U-Net model trained using 5-fold cross-validation to make the model robust. B. The isolated cortical bones, shown as grayscale images that maintain the original pixel values, are then used for GLCM feature extraction. These features are subsequently trained using 5-fold cross-validation and grid search for optimal performance. Subsequently, an XGBoost machine learning classification is employed to determine distinctive attributes between non-CKD and CKD cortical bones. Model 1 = Distal radius; Model 2 = Diaphyseal radius; Model 3 = Distal tibia; Model 4 = Diaphyseal tibia.