Development and validation of an interpretable machine learning model for osteoporosis prediction using routine blood tests: a retrospective cohort study.

ABSTRACT:

Background

While dual-energy X-ray absorptiometry (DXA) remains the gold standard for osteoporosis diagnosis, its clinical utility is constrained by cost and accessibility challenges. This study aims to develop a predictive model for osteoporosis using routinely available clinical blood biomarkers, thereby providing an innovative and accessible approach for early detection.

Methods

We retrospectively analyzed 8,144 orthopedic inpatients who underwent DXA scans at Panyu Hospital of Guangzhou University of Chinese Medicine between January 2022 and December 2023. Demographic characteristics and first 24-hour admission blood parameters were collected. Potential predictors were identified through univariate analysis, least absolute shrinkage and selection operator (LASSO) regress

SUBMITTER: Wei Q 

PROVIDER: S-EPMC12752015 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature