ArticleEuropean geriatric medicine2026
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.
Article in European geriatric medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeSarcopenia 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.
methodsWe 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.
resultsOn temporal validation, the model achieved strong performance with Pearson r = 0.806, R
conclusionsThis 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.
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