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ID 114035
Title Alternative
Texture analysis of myopathy
Author
Nodera, Hiroyuki Tokushima University|Kanazawa Medical University KAKEN Search Researchers
Sogawa, Kazuki Tokushima University
Takamatsu, Naoko Tokushima University
Hashiguchi, Shuji Tokushima University
Saito, Miho Tokushima Hospital
Mori, Atsuko Tokushima University|Itsuki Hospital
Keywords
myopathy
texture analysis
muscle ultrasound
machine learning
Content Type
Journal Article
Description
Given the recent technological advent of muscle ultrasound (US), classification of various myopathic conditions could be possible, especially by mathematical analysis of muscular fine structure called texture analysis. We prospectively enrolled patients with three neuromuscular conditions and their lower leg US images were quantitatively analyzed by texture analysis and machine learning methodology in the following subjects : Inclusion body myositis (IBM) [N=11] ; myotonic dystrophy type 1 (DM1) [N=19] ; polymyositis/dermatomyositis (PM-DM) [N=21]. Although three-group analysis achieved up to 58.8% accuracy, two-group analysis of IBM plus PM-DM versus DM1 showed 78.4% accuracy. Despite the small number of subjects, texture analysis of muscle US followed by machine learning might be expected to be useful in identifying myopathic conditions.
Journal Title
The Journal of Medical Investigation
ISSN
13496867
13431420
NCID
AA12022913
AA11166929
Publisher
Tokushima University Faculty of Medicine
Volume
66
Issue
3-4
Start Page
237
End Page
240
Sort Key
237
Published Date
2019-08
EDB ID
DOI (Published Version)
URL ( Publisher's Version )
FullText File
language
eng
TextVersion
Publisher
departments
Medical Sciences
University Hospital