Application of Artificial Intelligence to Automate the Reconstruction of Muscle Cross-Sectional Area Obtained by Ultrasound.

Abstract

Manual reconstruction (MR) of the vastus lateralis (VL) muscle cross sectional area (CSA) from sequential ultrasound (US) images is accessible, reproducible and has concurrent validity with magnetic resonance imaging. However, this technique requires numerous controls and procedures during image acquisition and reconstruction, making it laborious and time-consuming. The aim of this study was to determine the concurrent validity of VL CSA assessments between MR and computer vision-based automatic reconstruction (AR) of CSA from sequential images of the VL obtained by US. The images from each sequence were manually rotated to align the fascia between images and thus visualize the VL CSA. For the AR, an artificial neural network model was utilized to segment areas of interest in the image, such as skin, fascia, deep aponeurosis, and femur. This segmentation was crucial to impose necessary constraints for the main assembly phase. At this stage, an image registration application, combined with differential evolution, was employed to achieve appropriate adjustments between the images. Next, the VL CSA obtained from the MR (n = 488) and AR (n = 488) techniques were used to determine their concurrent validity. Our findings demonstrated a low coefficient of variation (CV) (1.51%) for AR compared to MR. The Bland-Altman plot showed low bias and close limits of agreement (+1.18 cm2, -1.19 cm2), containing more than 95% of the data points. The AR technique is valid compared to MR when measuring VL CSA in a heterogeneous sample.