Texture analysis of sonographic muscle images can distinguish myopathic conditions.
machine learning
muscle ultrasound
myopathy
texture analysis
Journal
The journal of medical investigation : JMI
ISSN: 1349-6867
Titre abrégé: J Med Invest
Pays: Japan
ID NLM: 9716841
Informations de publication
Date de publication:
2019
2019
Historique:
entrez:
29
10
2019
pubmed:
28
10
2019
medline:
9
6
2020
Statut:
ppublish
Résumé
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. J. Med. Invest. 66 : 237-240, August, 2019.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM