Multimodal Radiomics and Deep Learning Integration for Bone Health Assessment in Postmenopausal Women via Dental Radiographs: Development of an Interpretable Nomogram

Abstract

ABSTRACT To develop and validate a multimodal machine learning model for opportunistic osteoporosis screening in postmenopausal women using dental periapical radiographs. This retrospective multicenter study analyzed 3885 periapical radiographs paired with DEXA‐derived T ‐scores from postmenopausal women. Clinical, handcrafted radiomic, and deep features were extracted, resulting in a fused feature set. Radiomic features ( n = 215) followed Image Biomarker Standardization Initiative (IBSI) guidelines, and deep features ( n = 128) were derived from a novel attention‐based autoencoder. Feature harmonization used ComBat adjustment; reliability was ensured by intra‐class correlation coefficient (ICC) filtering (ICC ≥ 0.80). Dimensionality was reduced via Pearson correlation and LASSO regression. Four classifiers—logistic regression, random forest, multilayer perceptron, and XGBoost—were trained and evaluated across stratified training, internal, and external test sets. A logistic regression model was selected for clinical translation and nomogram development. Decision curve analysis assessed clinical utility. XGBoost achieved the highest classification performance using the fused feature set, with an internal AUC of 94.6% and external AUC of 93.7%. Logistic regression maintained strong performance (external AUC = 91.3%) and facilitated nomogram construction. Deep and radiomic features independently outperformed clinical‐only models, confirming their predictive strength. SHAP analysis identified DEXA T ‐score, age, vitamin D, and selected radiomic/deep features as key contributors. Calibration curves and Hosmer–Lemeshow test ( p = 0.492) confirmed model reliability. Decision curve analysis showed meaningful net clinical benefit across decision thresholds. Dental periapical radiographs can be leveraged for accurate, non‐invasive osteoporosis screening in postmenopausal women. The proposed model demonstrates high accuracy, generalizability, and interpretability, offering a scalable solution for integration into dental practice.

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