Texture analysis of sonographic muscle images can distinguish myopathic conditions

Nodera, Hiroyuki; Sogawa, Kazuki; Takamatsu, Naoko; Hashiguchi, Shuji; Saito, Miho; Mori, Atsuko; Osaki, Yusuke; Izumi, Yuishin; Kaji, Ryuji

doi:10.2152/jmi.66.237

Cited by 17 publications

(9 citation statements)

References 10 publications

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“…Given some challenges surrounding the assessment of echogenicity in muscle, other techniques have also been applied to the analysis of muscle US images that do not take into account echogenicity alone. These have included texture analysis as well as machine learning methods [ 48 – 50 , 51 •, 52 ]. Deep learning methods are particularly interesting as they may provide more objective means for disease characterization that takes into account information from the whole image as opposed to the muscle alone.…”

Section: Emerging Use In Myositismentioning

confidence: 99%

Diagnostic Value of Muscle Ultrasound for Myopathies and Myositis

Albayda

Alfen

2020

Curr Rheumatol Rep

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Purpose of Review The purpose of this review is to critically discuss the use of ultrasound in the evaluation of muscle disorders with a particular focus on the emerging use in inflammatory myopathies. Recent Findings In myopathies, pathologic muscle shows an increase in echogenicity. Muscle echogenicity can be assessed visually, semi-quantitatively, or quantitatively using grayscale analysis. The involvement of specific muscle groups and the pattern of increase in echogenicity can further point to specific diseases. In pediatric neuromuscular disorders, the value of muscle ultrasound for screening and diagnosis is well-established. It has also been found to be a responsive measure of disease change in muscular dystrophies. In chronic forms of myositis like inclusion body myositis, ultrasound is very suitable for detecting markedly increased echogenicity and atrophy in affected muscles. Acute cases of muscle edema show only a mild increase in echogenicity, which can also reverse with successful treatment. Summary Muscle ultrasound is an important imaging modality that is highly adaptable to study various muscle conditions. Although its diagnostic value for neuromuscular disorders is high, the evidence in myositis has only begun to accrue in earnest. Further systematic studies are needed, especially in its role for detecting muscle edema.

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Section: Emerging Use In Myositismentioning

confidence: 99%

Diagnostic Value of Muscle Ultrasound for Myopathies and Myositis

Albayda

Alfen

2020

Curr Rheumatol Rep

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“…Given the emerging importance of muscle MR in the diagnostic process of congenital myopathies, the development of a computed automatic single muscle scoring system is becoming relevant. In the literature multiple attempts of applying texture analyses on specific myopathy are reported and showed, for example, their capability in distinguishing normal and pathologic muscles in neurogenic and myopathic diseases [ 18 ], in distinguishing inflammatory myopathy from myotonic dystrophy [ 19 ], identifying the progression of disease [ 20 ], quantifying muscle T2 water and fat fraction in facio-scapulo-humeral dystrophy [ 21 ], and in discerning between different types of inflammatory myopathies [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

Muscle magnetic resonance characterization of STIM1 tubular aggregate myopathy using unsupervised learning

et al. 2023

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Purpose Congenital myopathies are a heterogeneous group of diseases affecting the skeletal muscles and characterized by high clinical, genetic, and histological variability. Magnetic Resonance (MR) is a valuable tool for the assessment of involved muscles (i.e., fatty replacement and oedema) and disease progression. Machine Learning is becoming increasingly applied for diagnostic purposes, but to our knowledge, Self-Organizing Maps (SOMs) have never been used for the identification of the patterns in these diseases. The aim of this study is to evaluate if SOMs may discriminate between muscles with fatty replacement (S), oedema (E) or neither (N). Methods MR studies of a family affected by tubular aggregates myopathy (TAM) with the histologically proven autosomal dominant mutation of the STIM1 gene, were examined: for each patient, in two MR assessments (i.e., t0 and t1, the latter after 5 years), fifty-three muscles were evaluated for muscular fatty replacement on the T1w images, and for oedema on the STIR images, for reference. Sixty radiomic features were collected from each muscle at t0 and t1 MR assessment using 3DSlicer software, in order to obtain data from images. A SOM was created to analyze all datasets using three clusters (i.e., 0, 1 and 2) and results were compared with radiological evaluation. Results Six patients with TAM STIM1-mutation were included. At t0 MR assessments, all patients showed widespread fatty replacement that intensifies at t1, while oedema mainly affected the muscles of the legs and appears stable at follow-up. All muscles with oedema showed fatty replacement, too. At t0 SOM grid clustering shows almost all N muscles in Cluster 0 and most of the E muscles in Cluster 1; at t1 almost all E muscles appear in Cluster 1. Conclusion Our unsupervised learning model appears to be able to recognize muscles altered by the presence of edema and fatty replacement.

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“…This tool can be used for choroidal flow assessment in order to provide a more comprehensive description of disease progression. Finally, authors in [ 67 ] used RF methodology in patients with three groups of rare myopathic conditions, which includes any disease that affects the muscles that control voluntary movement, showing that the methodology was able to classify myotonic dystrophy type 1 and inflammatory myopathy. Table 1 summaries the described above ML methods applied for the diagnosis of rare diseases.…”

Section: Ai Application In Rare Diseasesmentioning

confidence: 99%

The Impact of Artificial Intelligence in the Odyssey of Rare Diseases

et al. 2023

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Emerging machine learning (ML) technologies have the potential to significantly improve the research and treatment of rare diseases, which constitute a vast set of diseases that affect a small proportion of the total population. Artificial Intelligence (AI) algorithms can help to quickly identify patterns and associations that would be difficult or impossible for human analysts to detect. Predictive modeling techniques, such as deep learning, have been used to forecast the progression of rare diseases, enabling the development of more targeted treatments. Moreover, AI has also shown promise in the field of drug development for rare diseases with the identification of subpopulations of patients who may be most likely to respond to a particular drug. This review aims to highlight the achievements of AI algorithms in the study of rare diseases in the past decade and advise researchers on which methods have proven to be most effective. The review will focus on specific rare diseases, as defined by a prevalence rate that does not exceed 1–9/100,000 on Orphanet, and will examine which AI methods have been most successful in their study. We believe this review can guide clinicians and researchers in the successful application of ML in rare diseases.

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Texture analysis of sonographic muscle images can distinguish myopathic conditions

Cited by 17 publications

References 10 publications

Diagnostic Value of Muscle Ultrasound for Myopathies and Myositis

Diagnostic Value of Muscle Ultrasound for Myopathies and Myositis

Muscle magnetic resonance characterization of STIM1 tubular aggregate myopathy using unsupervised learning

The Impact of Artificial Intelligence in the Odyssey of Rare Diseases

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