An Automated Assessment Method for Chronic Kidney Disease–Mineral and Bone Disorder (CKD-MBD) Utilizing Metacarpal Cortical Percentage

Abstract

Chronic kidney disease–mineral and bone disorder (CKD-MBD) frequently occurs in hemodialysis patients and is a common cause of osteoporosis. Regular dual-energy X-ray absorptiometry (DXA) scans are used to monitor these patients, but frequent, cost-effective, and low-dose alternatives are needed. This study proposes an automatic CKD-MBD assessment model using histogram equalization and a squeeze-and-excitation block-based residual U-Net (SER-U-Net) with hand diagnostic radiography for preliminary classification. The process involves enhancing image contrast with histogram equalization, extracting features with the SE-ResNet model, and segmenting metacarpal bones using U-Net. Ultimately, a correlation analysis is carried out between the calculated dual metacarpal cortical percentage (dMCP) and DXA T-scores. The model’s performance was validated by analyzing clinical data from 30 individuals, achieving a 93.33% accuracy in classifying bone density compared to DXA results. This automated method provides a rapid, effective tool for CKD-MBD assessment in clinical settings.