Texture analysis of sonographic muscle images can distinguish myopathic conditions

被引：19

作者：

Nodera, Hiroyuki ^{[1
,5
]}

Sogawa, Kazuki ^{[3
]}

Takamatsu, Naoko ^{[1
]}

Hashiguchi, Shuji ^{[2
]}

Saito, Miho ^{[2
]}

Mori, Atsuko ^{[1
,4
]}

Osaki, Yusuke ^{[1
]}

Izumi, Yuishin ^{[1
]}

Kaji, Ryuuji ^{[1
]}

机构：

[1] Tokushima Univ, Dept Neurol, Tokushima, Japan

[2] Tokushima Hosp, Tokushima, Japan

[3] Tokushima Univ, Fac Med, Tokushima, Japan

[4] Itsuki Hosp, Tokushima, Japan

[5] Kanazawa Med Univ, Dept Neurol, Kanazawa, Ishikawa, Japan

来源：

JOURNAL OF MEDICAL INVESTIGATION | 2019年 / 66卷 / 3-4期

关键词：

myopathy; texture analysis; muscle ultrasound; machine learning; ECHOGENICITY;

D O I：

10.2152/jmi.66.237

中图分类号：

R-3 [医学研究方法]; R3 [基础医学];

学科分类号：

1001 ;

摘要：

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.

引用

页码：237 / 240

页数：4

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