Observational studyJournal of cachexia, sarcopenia and muscle2025
Evaluation of Muscle Mass and Quality With an AI-Based Muscle Ultrasound Imaging System in Patients at Risk of Malnutrition.
Observational study in Journal of cachexia, sarcopenia and muscle, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The role of muscle ultrasound in stroke rehabilitation: a review of calf muscle alterations and clinical implications.Frontiers in neurologyPooled it
- Artificial Intelligence-Based Muscle Ultrasound: A Novel Approach to Nutritional Evaluation Beyond Quantity in Neurological Patients.Nutrients · 2026Observational
- Artificial Intelligence in Clinical Nutrition: Current Uses, Challenges, and Opportunities.Nutrients · 2026Review
- Undetected Weight Loss Associates With Upstaging in Cancer Patients.Journal of cachexia, sarcopenia and muscle · 2026Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSarcopenia is characterized by the loss of muscle mass, quality and function. Ultrasonography provides a non-invasive method for assessing sarcopenia. Its generalizability remains limited due to certain methodological and population-specific challenges. This study evaluated the association between AI-assisted muscle ultrasonography and sarcopenia in patients at risk of malnutrition.
methodsThis observational, cross-sectional study included 647 patients at risk of malnutrition. Nutritional status was assessed via anthropometry, bioimpedanciometry, quadriceps rectus femoris (QRF) ultrasonography and handgrip strength. An AI-based imaging system segmented the region of interest (ROI) in transverse QRF images to measure muscle thickness (RFMT), area (RFMA) and pennation angle (RFPA). The Multi-Otsu algorithm extracted ROI biomarkers: low echogenicity (MiT) and medium echogenicity (FatiT), assumed as a surrogate of muscle and fat percentage of the ROI. Sarcopenia was diagnosed using European Working Group on Sarcopenia in Older People (EWGSOP2) criteria and malnutrition was assessed with Global Leadership Initiative on Malnutrition (GLIM) criteria.
resultsMost of the patients of the study were female (54.4%) and the mean age was 64.83 ± 15.79 years. Malnutrition was present in 530 patients (81.9%) and sarcopenia in 167 patients (25.8%) Among patients with sarcopenia 57.2% had low muscle mass, and 44% had low handgrip strength. Patients with sarcopenia had significantly lower values of RFMT (sarcopenia: 0.89 ± 0.27 cm; no sarcopenia: 1.03 + 0.29 cm; p < 0.01) and RFMA (sarcopenia: 2.77 + 1.02 cm²; no sarcopenia: 3.25 + 1.17 cm²; p < 0.01). In terms of muscle quality by AI-assisted ultrasonography, we observed lower values of pennation angle (sarcopenia: 4.97 ± 2.91°; no sarcopenia: 5.50 ± 2.78°; p < 0.01), low echogenicity (MiT) (sarcopenia: 45 ± 10.80%; no sarcopenia: 47.39 ± 10.91%; p = 0.02) and a higher high echogenicity percentage (NMNFiT) (sarcopenia: 14.99 ± 5.52%; no sarcopenia: 14.76 ± 5.17%; p = 0.02). Multivariate analysis showed male sex as a risk factor for sarcopenia (OR = 1.85 (IC 95%: 1.23-2.77); p < 0.01), while higher RFMT was protective (OR: 0.18 (IC 95%: 0.04-0.86); p = 0.03). For low handgrip strength, higher MiT was protective (OR: 0.07 (IC 95%: 0.13-0.43); p < 0.01) after adjusting for age and sex.
conclusionsIn patients at risk of malnutrition, sarcopenia and dynapenia were associated with reduced muscle mass and quality. AI-based ultrasound parameters, particularly RFMT and MiT, were significantly lower in individuals with sarcopenia and correlated with poorer muscle function, independent of age and sex.
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