RT info:eu-repo/semantics/article
T1 Validation of an Artificial Intelligence-Based Ultrasound Imaging System for Quantifying Muscle Architecture Parameters of the Rectus Femoris in Disease-Related Malnutrition (DRM)
A1 de luis roman, Daniel
AB The aim was to validate an AI-based system compared to the classic methodof reading ultrasound images of the rectus femur (RF) muscle in a real cohort of patients with diseaserelatedmalnutrition. (2) Methods: One hundred adult patients with DRM aged 18 to 85 years wereenrolled. The risk of DRM was assessed by the Global Leadership Initiative on Malnutrition (GLIM).The variation, reproducibility, and reliability of measurements for the RF subcutaneous fat thickness(SFT), muscle thickness (MT), and cross-sectional area (CSA), were measured conventionally withthe incorporated tools of a portable ultrasound imaging device (method A) and compared with theautomated quantification of the ultrasound imaging system (method B). (3) Results: Measurementsobtained using method A (i.e., conventionally) and method B (i.e., raw images analyzed by AI),showed similar values with no significant differences in absolute values and coefficients of variation,58.39–57.68% for SFT, 30.50–28.36% for MT, and 36.50–36.91% for CSA, respectively. The IntraclassCorrelation Coefficient (ICC) for reliability and consistency analysis betweenmethods A and B showedcorrelations of 0.912 and 95% CI [0.872–0.940] for SFT, 0.960 and 95% CI [0.941–0.973] forMT, and 0.995and 95% CI [0.993–0.997] for CSA; the Bland–Altman Analysis shows that the spread of points is quiteuniform around the bias lines with no evidence of strong bias for any variable. (4) Conclusions: Thestudy demonstrated the consistency and reliability of this new automatic system based on machinelearning and AI for the quantification of ultrasound imaging of the muscle architecture parameters ofthe rectus femoris muscle compared with the conventional method of measurement
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/73691
UL https://uvadoc.uva.es/handle/10324/73691
LA eng
NO Nutrients 2024, 16, 1806.
DS UVaDOC
RD 05-feb-2025