Deep convolutional neural network-based algorithm for muscle biopsy diagnosis

Kabeya, Yoshinori; Okubo, Mariko; Yonezawa, S.; Nakano, H.; Inoue, Michio; Ogasawara, Masashi; Ohsawa, Yutaka; Tanboon, Jantima; Indrawati, Luh Ari; Kumutpongpanich, Theerawat; Chen, Yen‐Lin; Yoshioka, Wakako; Hayashi, Shinichiro; Iwamori, Toshiya; Takeuchi, Yusuke; Tokumasu, Reitaro; Takano, Atsushi; Matsuda, Fumihiko; Nishino, Ichizo

doi:10.1038/s41374-021-00647-w

Cited by 8 publications

(6 citation statements)

References 24 publications

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“…An artificial intelligence algorithm based on deep convolutional neural networks was trained to differentiate IIM from hereditary muscle diseases (HMD) in microscopic images of haematoxylin-and-eosin-stained pathology slides. The trained algorithm managed to differentiate IIM from HMD with better sensitivity and specificity than nine physicians and successfully and accurately classified four subtypes of IIM (60). These results support the reliability of the algorithm and suggest that it has the potential to be used in a clinical setting.…”

Section: Histologysupporting

confidence: 61%

Idiopathic inflammatory myopathies: one year in review 2022

Cardelli

Zanframundo

Cometi

et al. 2023

Clinical and Experimental Rheumatology

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Idiopathic inflammatory myopathies (IIM) are a heterogeneous group of disorders in which chronic inflammation of the skeletal muscle, leading to muscle weakness, is a common feature. Different phenotypes have been identified within the IIM spectrum based on extra-muscular manifestations, immunology, muscle histology, responsiveness to therapy, and prognosis. The pathogenesis, classification, treatment, and prognosis of the different IIM subtypes are subject to active discussion and research. This review highlights the most relevant literature published on this topic over the last year.

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Section: Histologysupporting

confidence: 61%

Idiopathic inflammatory myopathies: one year in review 2022

Cardelli

Zanframundo

Cometi

et al. 2023

Clinical and Experimental Rheumatology

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“…Automated learning algorithms have been successfully evaluated in several types of cancer [ 22 , 23 , 24 ] and in microbiology [ 25 ], adopting pathological studies of tissues. As regards the muscle tissue, automated learning algorithms have been successfully evaluated in immunofluorescence images in several studies [ 26 , 27 ], and there are very few papers regarding histological images [ 28 , 29 ]. Each of these reports utilized algorithms able to recognize and segment cellular and environmental details and achieved good accuracy and performance.…”

Section: Discussionmentioning

confidence: 99%

Automatic Recognition of Ragged Red Fibers in Muscle Biopsy from Patients with Mitochondrial Disorders

Baldacci¹,

Calderisi²,

Santorelli

et al. 2022

Healthcare

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Mitochondrial dysfunction is considered to be a major cause of primary mitochondrial myopathy in children and adults, as reduced mitochondrial respiration and morphological changes such as ragged red fibers (RRFs) are observed in muscle biopsies. However, it is also possible to hypothesize the role of mitochondrial dysfunction in aging muscle or in secondary mitochondrial dysfunctions. The recognition of true histological patterns of mitochondrial myopathy can avoid unnecessary genetic investigations. The aim of our study was to develop and validate machine-learning methods for RRF detection in light microscopy images of skeletal muscle tissue. We used image sets of 489 color images captured from representative areas of Gomori’s trichrome-stained tissue retrieved from light microscopy images at a 20× magnification. We compared the performance of random forest, gradient boosting machine, and support vector machine classifiers. Our results suggested that the advent of scanning technologies, combined with the development of machine-learning models for image classification, make neuromuscular disorders’ automated diagnostic systems a concrete possibility.

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“…In many data processing and big data problems, AI methods are widely used for classification [11], clustering [12], detection [13], and prediction [14,15]. AI is a broad term that encompasses a variety of techniques, including fuzzy methods [16], neural network-based algorithms [17], heuristic algorithms [18], machine learning [19], and deep learning [20]. This study focuses on the prediction of crude oil data and the results obtained using machine learning and deep learning methods are compared.…”

Section: Artificial Intelligence Methodsmentioning

confidence: 99%

Artificial Intelligence-Based Prediction of Crude Oil Prices Using Multiple Features under the Effect of Russia–Ukraine War and COVID-19 Pandemic

et al. 2022

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The effect of the COVID-19 pandemic on crude oil prices just faded; at this moment, the Russia–Ukraine war brought a new crisis. In this paper, a new application is developed that predicts the change in crude oil prices by incorporating these two global effects. Unlike most existing studies, this work uses a dataset that involves data collected over twenty-two years and contains seven different features, such as crude oil opening, closing, intraday highest value, and intraday lowest value. This work applies cross-validation to predict the crude oil prices by using machine learning algorithms (support vector machine, linear regression, and rain forest) and deep learning algorithms (long short-term memory and bidirectional long short-term memory). The results obtained by machine learning and deep learning algorithms are compared. Lastly, the high-performance estimation can be achieved in this work with the average mean absolute error value over 0.3786.

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Deep convolutional neural network-based algorithm for muscle biopsy diagnosis

Cited by 8 publications

References 24 publications

Idiopathic inflammatory myopathies: one year in review 2022

Idiopathic inflammatory myopathies: one year in review 2022

Automatic Recognition of Ragged Red Fibers in Muscle Biopsy from Patients with Mitochondrial Disorders

Artificial Intelligence-Based Prediction of Crude Oil Prices Using Multiple Features under the Effect of Russia–Ukraine War and COVID-19 Pandemic

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