Texture analysis of sonographic muscle images can distinguish myopathic conditions.


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
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.

Identifiants

pubmed: 31656281
doi: 10.2152/jmi.66.237
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

237-247

Auteurs

Hiroyuki Nodera (H)

Department of Neurology, Tokushima University, Tokushima, Japan.
Department of Neurology, Kanazawa Medical University, Ishikawa, Japan.

Kazuki Sogawa (K)

Faculty of Medicine, Tokushima University, Tokushima, Japan.

Naoko Takamatsu (N)

Department of Neurology, Tokushima University, Tokushima, Japan.

Shuji Hashiguchi (S)

Tokushima Hospital, Tokushima, Japan.

Miho Saito (M)

Tokushima Hospital, Tokushima, Japan.

Atsuko Mori (A)

Department of Neurology, Tokushima University, Tokushima, Japan.
Itsuki Hospital, Tokushima, Japan.

Yusuke Osaki (Y)

Department of Neurology, Tokushima University, Tokushima, Japan.

Yuishin Izumi (Y)

Department of Neurology, Tokushima University, Tokushima, Japan.

Ryuji Kaji (R)

Department of Neurology, Tokushima University, Tokushima, Japan.

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