Objectives Rheumatoid arthritis (RA) significantly increases the risk of osteoporosis (OP) and fractures, yet dual-energy X-ray absorptiometry (DXA) is underused in routine care. This study aims to develop and explain a machine learning model to identify individuals with OP risk among RA patients. Methods In this single-center retrospective study, 299 RA patients were enrolled and split 7:3 into training and test sets. Candidate features included clinical variables, laboratory indices, and musculoskeletal ultrasound semi-quantitative scores. After performing correlation analysis and variance inflation factor (VIF) screening, recursive feature elimination combined with random forest (RF) and five-fold cross-validation was used for feature selection. Nine machine learning models were constructed and compared in terms of performance. The optimal model was further validated through calibration curves, precision-recall (P–R) curves, and SHapley Additive exPlanations (SHAP) analysis. Results The extra trees (ET) model demonstrated the most stable performance, with an area under the curve (AUC) of 0.914 (95% CI 0.854–0.961), specificity of 0.951, sensitivity of 0.586, accuracy of 0.833, and a Brier score of 0.120 in the test set. Calibration and P–R curve analyses indicated good model performance. SHAP analysis revealed bone erosion (BE), age, and disease duration as key driving factors for RA-OP. Conclusions An interpretable machine learning model integrating clinical variables, laboratory indices, and semi-quantitative ultrasound scores may support risk stratification for DXA-defined osteoporosis in patients with RA. Given its high specificity but moderate sensitivity, the model should be regarded as an adjunctive tool for prioritizing DXA assessment and bone-health management rather than as a stand-alone screening method. <Table Float="No" ID="Taba"> <tgroup cols="2"> <colspec align="left" colname="c1" colnum="1" /> <colspec align="left" colname="c2" colnum="2" /> <tbody> <row> <entry align="left" nameend="c2" namest="c1"> Key Points • An explainable machine learning model integrating clinical variables and semi-quantitative ultrasound scores was developed for RA-specific OP risk stratification. • The final extra trees model demonstrated good discrimination and high specificity, but moderate sensitivity, supporting its use as an adjunctive rather than stand-alone screening tool. • Semi-quantitative ultrasound scores may provide complementary information beyond routine clinical indicators for prioritizing DXA assessment. </entry> </row> </tbody> </tgroup> </Table>

An interpretable machine learning model integrating ultrasound and clinical variables for predicting osteoporosis in patients with rheumatoid arthritis

期刊论文
发表日期：

2026年7月17日

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Kaiyi Yang,
Xinyu Gao,
Yaxin Deng,
Jiajun Hu,
Jing Xu,
Xiang Xu,
Jinshu Zeng,
Shuqiang Chen

摘要

Objectives

Rheumatoid arthritis (RA) significantly increases the risk of osteoporosis (OP) and fractures, yet dual-energy X-ray absorptiometry (DXA) is underused in routine care. This study aims to develop and explain a machine learning model to identify individuals with OP risk among RA patients.

Methods

In this single-center retrospective study, 299 RA patients were enrolled and split 7:3 into training and test sets. Candidate features included clinical variables, laboratory indices, and musculoskeletal ultrasound semi-quantitative scores. After performing correlation analysis and variance inflation factor (VIF) screening, recursive feature elimination combined with random forest (RF) and five-fold cross-validation was used for feature selection. Nine machine learning models were constructed and compared in terms of performance. The optimal model was further validated through calibration curves, precision-recall (P–R) curves, and SHapley Additive exPlanations (SHAP) analysis.

Results

The extra trees (ET) model demonstrated the most stable performance, with an area under the curve (AUC) of 0.914 (95% CI 0.854–0.961), specificity of 0.951, sensitivity of 0.586, accuracy of 0.833, and a Brier score of 0.120 in the test set. Calibration and P–R curve analyses indicated good model performance. SHAP analysis revealed bone erosion (BE), age, and disease duration as key driving factors for RA-OP.

Conclusions

An interpretable machine learning model integrating clinical variables, laboratory indices, and semi-quantitative ultrasound scores may support risk stratification for DXA-defined osteoporosis in patients with RA. Given its high specificity but moderate sensitivity, the model should be regarded as an adjunctive tool for prioritizing DXA assessment and bone-health management rather than as a stand-alone screening method.

Key Points

• An explainable machine learning model integrating clinical variables and semi-quantitative ultrasound scores was developed for RA-specific OP risk stratification.

• The final extra trees model demonstrated good discrimination and high specificity, but moderate sensitivity, supporting its use as an adjunctive rather than stand-alone screening tool.

• Semi-quantitative ultrasound scores may provide complementary information beyond routine clinical indicators for prioritizing DXA assessment.