COMBINING CHEST X-RAY AND CLINICAL COVARIATES FOR OSTEOPOROSIS SCREENING: A MULTIMODAL DEEP LEARNING APPROACH

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Abstract

Osteoporosis is a major global health concern, yet screening remains inaccessible in many economically challenged and low-resource settings due to the high cost and operational complexity the gold standard Dual-energy X-ray Absorptiometry (DXA) scan requires. This study presents a multimodal deep-learning approach for opportunistic osteoporosis screening using chest X-rays and clinical variables such as age and gender. A total of 406 samples were retrospectively collected from a national hospital, comprising annotated chest radiographs paired with their DXA results from patients 50 and above. Two convolutional neural network (CNN) architectures, ResNet50 and EfficientNetb3, were deployed under three configurations: image-only, early fusion, and late fusion. Image preprocessing includes CLAHE+Gamma Correction, followed by resizing, local regions of interest (ROI) extraction, and augmentation. The multimodal integration was conducted via early fusion (pixel-level) and late fusion (feature-level), with clinical variables normalized and encoded into a 1x3 vector. A custom Residual MLP classifier was employed in the late fusion models. Hyperparameter tuning was performed using Optunas Bayesian optimization. Evaluation metrics included Accuracy, AUC, Precision, Recall, Sensitivity, and F1 Score. Among all models, Late Fusion EfficientNetb3 achieved the best performance on the global chest X-ray, with an AUC of 72.73%. Moreover, EfficientNetb3 consistently outperformed the ResNet50 architecture across all local ROI, Right Clavicle, Right Scapula, and Spine, with fusion models yielding higher sensitivity and balanced classification. The framework achieved competitive diagnostic performance despite the limitations of chest radiographs and an imbalanced dataset. Moreover, the findings demonstrated the feasibility of integrating chest X-rays with clinical covariates for AI-assisted osteoporosis screening, aiming to fill the accessibility gaps in preventive bone health diagnostics.

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