Evaluation of a Fully Automatic Deep Learning-Based Method for the Measurement of Psoas Muscle Area.

ABSTRACT:

Background

Manual muscle mass assessment based on Computed Tomography (CT) scans is recognized as a good marker for malnutrition, sarcopenia, and adverse outcomes. However, manual muscle mass analysis is cumbersome and time consuming. An accurate fully automated method is needed. In this study, we evaluate if manual psoas annotation can be substituted by a fully automatic deep learning-based method.

Methods

This study included a cohort of 583 patients with severe aortic valve stenosis planned to undergo Transcatheter Aortic Valve Replacement (TAVR). Psoas muscle area was annotated manually on the CT scan at the height of lumbar vertebra 3 (L3). The deep learning-based method mimics this approach by first determining the L3 level and subsequently segmenting the psoas at that

SUBMITTER: Van Erck D 

PROVIDER: S-EPMC9133929 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature