Explainable machine learning for osteoporosis detection in patients with osteopenia: model development and validation using routine clinical data from an Asian cohort.

Authors

Xiuzhen Zhang, Li Zhao, Han Wu, Fengyi Yuan, Weiqing Wu, Yan Wu, Wei Wang

Published in

Frontiers in endocrinology. Volume 17. Pages 1857227. Epub Jul 20, 2026.

Abstract

Osteopenia is a critical precursor to osteoporosis (OP), yet accurately discriminating OP from osteopenia among individuals with low bone mass remains challenging. While dual-energy X-ray absorptiometry (DXA) provides definitive diagnosis, accessibility limitations necessitate alternative screening approaches. We therefore aimed to develop an algorithm based on readily available clinical data to discriminate between OP and osteopenia in this population.
We conducted a retrospective diagnostic study to develop a model for discriminating osteoporosis from osteopenia within a cohort of 1,203 Asian adults with low bone mass. Eleven machine learning algorithms were trained and validated for this diagnostic task (case: osteoporosis [T-score ≤ -2.5]; control: osteopenia [T-score -2.5 to -1.0]).Performance was evaluated using area under the curve (AUC). The interpretability and clinical utility of the selected model were respectively enhanced and validated by SHAP analysis, nomogram calibration, and decision curve analysis (DCA).
The Linear Discriminant Analysis model demonstrated superior and consistent performance. It achieved a mean cross-validated AUC of 0.738 (95% CI: 0.736-0.741) and showed excellent calibration (Hosmer-Lemeshow p = 0.266). On an independent validation set, the model maintained robust performance with an AUC of 0.710 (95% CI: 0.686-0.734). Key predictors included waist-to-height ratio, body weight, serum uric acid, age, and alkaline phosphatase. DCA indicated a positive net benefit across a wide range of risk thresholds.
This study developed a practical and interpretable tool for discriminating osteoporosis from osteopenia among individuals with low bone mass, using only routinely available clinical data. This approach may serve as a preliminary screening tool to identify high-risk individuals within primary care populations for further definitive testing.

PMID:
42548658
Bibliographic data and abstract were imported from PubMed on 04 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.