In Medical physics ; h5-index 59.0
BACKGROUND : Measurement of cross-sectional muscle area (CSMA) at the mid third lumbar vertebra (L3) level from computed tomography (CT) images is becoming one of the reference methods for sarcopenia diagnosis. However, manual skeletal muscle segmentation is tedious and is thus restricted to research. Automated solutions are required for use in clinical practice.
PURPOSE : The aim of this study was to compare the reliability of two automated solutions for the measurement of CSMA.
METHODS : We conducted a retrospective analysis of CT images in our hospital database. We included consecutive individuals hospitalized at the Grenoble University Hospital in France between January and May 2018 with abdominal CT images and sagittal reconstruction. We used 2 types of software to automatically segment skeletal muscle: ABACS, a module of the SliceOmatic software solution 'ABACS-SliceOmatic', and a deep learning-based solution called 'AutoMATiCA'. Manual segmentation was performed by a medical expert to generate reference data using 'SliceOmatic'. The Dice similarity coefficient (DSC) was used to measure overlap between the results of the manual and the automated segmentations. The DSC value for each method was compared with the Mann-Whitney U test.
RESULTS : A total of 676 hospitalized individuals was retrospectively included (365 males [53.8%] and 312 females [46.2%]). The median DSC for SliceOmatic vs AutoMATiCA (0.969 [5th percentile: 0.909]) was greater than the median DSC for SliceOmatic vs ABACS-SliceOmatic 0.949 ([5th percentile: 0.836]) (p<0.001) CONCLUSIONS: AutoMATiCA, which used artificial intelligence, was more reliable than ABACS-SliceOmatic for skeletal muscle segmentation at the L3 level in a cohort of hospitalized individuals. The next step is to develop and validate a neural network that can identify L3 slices, which is currently a fastidious process. This article is protected by copyright. All rights reserved.
Charrière Katia, Boulouard Quentin, Artemova Svetlana, Vilotitch Antoine, Ferretti Gilbert R, Bosson Jean-Luc, Moreau-Gaudry Alexandre, Giai Joris, Fontaine Eric, Bétry Cécile
2023-Feb-01
artificial intelligence, body composition, computational neural networks, sarcopenia, skeletal muscle, software validation
