Integrating deep learning and machine learning for improved CKD-related cortical bone assessment in HRpQCT images: A pilot study.

ABSTRACT: High resolution peripheral quantitative computed tomography (HRpQCT) offers detailed bone geometry and microarchitecture assessment, including cortical porosity, but assessing chronic kidney disease (CKD) bone images remains challenging. This proof-of-concept study merges deep learning and machine learning to 1) improve automatic segmentation, particularly in cases with severe cortical porosity and trabeculated endosteal surfaces, and 2) maximize image information using machine learning feature extraction to classify CKD-related skeletal abnormalities, surpassing conventional DXA and CT measures. We included 30 individuals (20 non-CKD, 10 stage 3 to 5D CKD) who underwent HRpQCT of the distal and diaphyseal radius and tibia and contributed data to develop and validate four different AI mode

SUBMITTER: Lee Y 

PROVIDER: S-EPMC11763521 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature