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Preoperative planning in shoulder arthroplasty: what about the soft tissue?

Authors :
Jean-David Werthel
François Boux de Casson
Cédric Manelli
Jean Chaoui
Gilles Walch
Valérie Burdin
Source :
EPiC Series in Health Sciences.
Publication Year :
2022
Publisher :
EasyChair, 2022.

Abstract

The primary objective of this study was to obtain a reliable method of automatic segmentation of shoulder muscles. The secondary objective of this study was to define a new computed tomography (CT)-based quantitative 3-dimensional (3D) measure of muscle loss (3DML) based on the rationale of the 2-dimensional (2D) qualitative Goutallier score. 102 CT scans were manually segmented and an algorithm of automated segmentation of the muscles was created. The volume of muscle fibers without intramuscular fat was then calculated for each rotator cuff muscle and normalized to the patient's scapular volume to account for the effect of body size (NVfibers). 3D muscle mass (3DMM) was calculated by dividing the NVfibers value of a given muscle by the mean expected volume in healthy shoulders. 3D muscle loss (3DML) was defined as 1 - (3DMM). Automated segmentation of the muscles was possible with a mean Dice of 0.904 ± 0.01 for the deltoid, 0.887 ± 0.014 for the infraspinatus (ISP), 0.892 ± 0.008 for the subscapularis (SSC), 0.885 for the supraspinatus (SSP) and 0.796 ± 0.006 for the teres minor (TM). The mean values of 3DFI and 3DML were 0.9% and 5.3% for Goutallier 0, 2.9% and 25.6% for Goutallier 1, 11.4% and 49.5% for Goutallier 2, 20.7% and 59.7% for Goutallier 3, and 29.3% and 70.2% for Goutallier 4, respectively. 3DML measurements obtained automatically incorporate both atrophy and fatty infiltration, thus they could become a very reliable index for assessing shoulder muscle function which could help in the decision process in shoulder surgery

Details

ISSN :
23985305
Database :
OpenAIRE
Journal :
EPiC Series in Health Sciences
Accession number :
edsair.doi...........29c22a8286cf4e4f893253571a04296c
Full Text :
https://doi.org/10.29007/x1n7