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Subject of Research: Medicine

Subject of Research: Medicine

Article Title: Machine learning improves bioimpedance estimates of skeletal muscle mass in older adults

Article References: Zanforlini, B. M., Biasetton, N., Perencin, A., Longo, G., Barzizza, E., Curreri, C., Bertocco, A., Ceolin, C., Sergi, G., Salmaso, L., & De Rui, M. (2026). Enhancing the accuracy of bioimpedance-derived appendicular skeletal muscle mass in aged adults through machine learning. European Geriatric Medicine. https://doi.org/10.1007/s41999-026-01592-x

Image Credits: AI Generated

DOI: 10.1007/s41999-026-01592-x

Keywords: advanced imaging and bioimpedance comparison, aging and muscle degeneration assessment, artificial intelligence in aging research, bioimpedance spectroscopy for muscle health, data-driven approaches to muscle mass estimation, health monitoring for elderly populations, machine learning algorithms for bioimpedance data, machine learning in bioimpedance analysis, non-invasive muscle mass measurement techniques, precision health in geriatrics, skeletal muscle mass estimation in older adults, wearable technology for muscle health

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Everett F. (August 29, 2026). Machine learning improves bioimpedance estimates of skeletal muscle mass in older adults. Scienmag. https://scienmag.com/machine-learning-improves-bioimpedance-estimates-of-skeletal-muscle-mass-in-older-adults/

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Tags: advanced imaging and bioimpedance comparisonaging and muscle degeneration assessmentaging-related muscle loss assessmentAI-enhanced muscle mass measurementartificial intelligence in aging researchbioimpedance spectroscopy for muscle healthdata-driven approaches to muscle mass estimationhealth monitoring for elderly populationsmachine learning algorithms for bioimpedance datamachine learning in bioimpedance analysisnon-invasive muscle health monitoringnon-invasive muscle mass measurement techniquesprecision health in geriatricsskeletal muscle mass estimation in older adultswearable technology for muscle health