Nutrients · 2025
Artificial Intelligence-Assisted Muscular Ultrasonography for Assessing Inflammation and Muscle Mass in Patients at Risk of Malnutrition.
СтатьяНаблюдательное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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López-Gómez Juan José, Estévez-Asensio Lucía, Cebriá Ángela, Izaola-Jauregui Olatz, Pérez López Paloma, González-Gutiérrez Jaime, Primo-Martín David, Jiménez-Sahagún Rebeca, Gómez-Hoyos Emilia, Rico-Bargues Daniel, Godoy Eduardo Jorge, De Luis-Román Daniel A
Аннотация
BACKGROUND: Malnutrition, influenced by inflammation, is associated with muscle depletion and body composition changes. This study aimed to evaluate muscle mass and quality using Artificial Intelligence (AI)-enhanced ultrasonography in patients with inflammation.
METHODS: This observational, cross-sectional study included 502 malnourished patients, assessed through anthropometry, electrical bioimpedanciometry, and ultrasonography of the quadriceps rectus femoris (QRF). AI-assisted ultrasonography was used to segment regions of interest (ROI) from transversal QRF images to measure muscle thickness (RFMT) and area (RFMA), while a Multi-Otsu algorithm was used to extract biomarkers for muscle mass (MiT) and fat mass (FatiT). Inflammation was defined as C-reactive protein (CRP) levels above 3 mg/L.
RESULTS: The results showed a mean patient age of 63.72 (15.95) years, with malnutrition present in 82.3% and inflammation in 44.8%. Oncological diseases were prevalent (46.8%). The 44.8% of patients with inflammation (CRP > 3) exhibited reduced RFMA (2.91 (1.11) vs. 3.20 (1.19) cm2, p < 0.01) and RFMT (0.94 (0.28) vs. 1.01 (0.30) cm, p < 0.01). Muscle quality was reduced, with lower MiT (45.32 (9.98%) vs. 49.10 (1.22%), p < 0.01) and higher FatiT (40.03 (6.72%) vs. 37.58 (5.63%), p < 0.01). Adjusted for age and sex, inflammation increased the risks of low muscle area (OR = 1.59, CI: 1.10-2.31), low MiT (OR = 1.49, CI: 1.04-2.15), and high FatiT (OR = 1.44, CI: 1.00-2.06).
CONCLUSIONS: AI-assisted ultrasonography revealed that malnourished patients with inflammation had reduced muscle area, thickness, and quality (higher fat content and lower muscle percentage). Elevated inflammation levels were associated with increased risks of poor muscle metrics. Future research should focus on exploring the impact of inflammation on muscles across various patient groups and developing AI-driven biomarkers to enhance the diagnosis, monitoring, and treatment of malnutrition and sarcopenia.
METHODS: This observational, cross-sectional study included 502 malnourished patients, assessed through anthropometry, electrical bioimpedanciometry, and ultrasonography of the quadriceps rectus femoris (QRF). AI-assisted ultrasonography was used to segment regions of interest (ROI) from transversal QRF images to measure muscle thickness (RFMT) and area (RFMA), while a Multi-Otsu algorithm was used to extract biomarkers for muscle mass (MiT) and fat mass (FatiT). Inflammation was defined as C-reactive protein (CRP) levels above 3 mg/L.
RESULTS: The results showed a mean patient age of 63.72 (15.95) years, with malnutrition present in 82.3% and inflammation in 44.8%. Oncological diseases were prevalent (46.8%). The 44.8% of patients with inflammation (CRP > 3) exhibited reduced RFMA (2.91 (1.11) vs. 3.20 (1.19) cm2, p < 0.01) and RFMT (0.94 (0.28) vs. 1.01 (0.30) cm, p < 0.01). Muscle quality was reduced, with lower MiT (45.32 (9.98%) vs. 49.10 (1.22%), p < 0.01) and higher FatiT (40.03 (6.72%) vs. 37.58 (5.63%), p < 0.01). Adjusted for age and sex, inflammation increased the risks of low muscle area (OR = 1.59, CI: 1.10-2.31), low MiT (OR = 1.49, CI: 1.04-2.15), and high FatiT (OR = 1.44, CI: 1.00-2.06).
CONCLUSIONS: AI-assisted ultrasonography revealed that malnourished patients with inflammation had reduced muscle area, thickness, and quality (higher fat content and lower muscle percentage). Elevated inflammation levels were associated with increased risks of poor muscle metrics. Future research should focus on exploring the impact of inflammation on muscles across various patient groups and developing AI-driven biomarkers to enhance the diagnosis, monitoring, and treatment of malnutrition and sarcopenia.