Rationale and Objectives To develop and evaluate an automated machine learning (ML)
algorithm for segmenting the paraspinous muscles on chest computed tomography (CT) …

Rationale and Objectives To assess whether a fully-automated deep learning system can
accurately detect and analyze truncal musculature at multiple lumbar vertebral levels and …

Objective: To investigate a fully automated abdominal CT-based muscle tool in a large adult
screening population. Methods: A fully automated validated muscle segmentation algorithm …

BACKGROUND. Sarcopenia is associated with adverse clinical outcomes. CT-based
skeletal muscle measurements for sarcopenia assessment are most commonly performed at …

AIM To develop a fully automated deep-learning-based approach to measure muscle area
for assessing sarcopenia on standard-of-care computed tomography (CT) of the abdomen …

Background Muscle metrics derived from computed tomography (CT) are associated with
adverse health events in older persons, but obtaining these metrics using current methods is …

The diagnosis of sarcopenia requires accurate muscle quantification. As an alternative to
manual muscle mass measurement through computed tomography (CT), artificial …

Purpose The purpose of this study was to build and train a deep convolutional neural
networks (CNN) algorithm to segment muscular body mass (MBM) to predict muscular …

Background Loss of muscle mass is a known feature of sarcopenia and predicts poor clinical
outcomes. Although muscle metrics can be derived from routine computed tomography (CT) …

Please see the Editorial Comment by Maria Pilar Aparisi Gómez discussing this article.
BACKGROUND. Skeletal muscle area (SMA), representing skeletal muscle cross-sectional …