European Geriatric Medicine · Mar 28, 2026
AI-driven appendicular skeletal muscle mass index (ASMI) prediction and low muscle mass detection from routine hip X-rays: a novel opportunistic case finding tool
By Lee L, Chuang SH, Kuo YJ et al.
PURPOSE: Sarcopenia diagnosis requires identifying low muscle mass (LMM), typically via dual-energy X-ray absorptiometry (DXA). However, DXA's limited accessibility restricts large-scale screening. This retrospective study aimed to develop and validate a deep learning model to predict DXA-derived ASMI from routine hip radiographs for opportunistic LMM case finding. METHODS: We included a selected hospital-based cohort of 1267 patients with both hip radiography and DXA scans, split into development (n = 1140) and temporal validation (n = 127) sets. A multimodal model integrating radiographic images (ResNet-34 backbone) and clinical variables (age, sex, height, weight, and BMI) was trained to predict continuous ASMI and classify LMM per the Asian Working Group for Sarcopenia (AWGS) 2019 criteria. RESULTS: On temporal validation, the model achieved strong performance with Pearson r = 0.806, R² = 0.631, MAE = 0.414 kg/m², and AUC = 0.874 for LMM classification. Applying AWGS diagnostic thresholds yielded sensitivity of 70.5% and specificity of 83.3%, with consistent performance across sex and age subgroups. Gradient-weighted Class Activation Mapping confirmed the focus on clinically relevant gluteal and proximal thigh muscles. CONCLUSIONS: This deep learning approach enables automated LMM identification from routine hip radiographs, offering a cost-effective, accessible tool for opportunistic LMM case finding and early intervention in at-risk populations.