ArticleFrontiers in endocrinology2026
Explainable machine learning for osteoporosis detection in patients with osteopenia: model development and validation using routine clinical data from an Asian cohort.
Article in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: 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. Methods: 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). Findings: 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. Interpretation: 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.