1. Texture analysis of sonographic muscle images can distinguish myopathic conditions
- Author
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Miho Saito, Hiroyuki Nodera, Shuji Hashiguchi, Ryuji Kaji, Kazuki Sogawa, Yusuke Osaki, Naoko Takamatsu, Atsuko Mori, and Yuishin Izumi
- Subjects
musculoskeletal diseases ,Adult ,Male ,Muscle ultrasound ,Texture (music) ,Polymyositis ,Myotonic dystrophy ,General Biochemistry, Genetics and Molecular Biology ,Dermatomyositis ,Myositis, Inclusion Body ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,medicine ,Humans ,Myotonic Dystrophy ,Prospective Studies ,Myopathy ,Muscle, Skeletal ,Aged ,Ultrasonography ,Aged, 80 and over ,business.industry ,General Medicine ,Middle Aged ,medicine.disease ,Myopathic Conditions ,030228 respiratory system ,030220 oncology & carcinogenesis ,Female ,medicine.symptom ,Inclusion body myositis ,Nuclear medicine ,business - Abstract
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.
- Published
- 2019