Geodesic
Applying radiomics in computed tomography data analysis to predict sarcopenia
Vestnik Sankt-Peterburgskogo universiteta. Prikladnaâ matematika, informatika, processy upravleniâ, Tome 20 (2024) no. 3, pp. 376-390

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This article presents an algorithm implementing a radiomics approach to processing computed tomography (CT) data for diagnosing sarcopenia. The proposed method includes region of interest extraction, automatic muscle segmentation using deep learning models, extraction of radiomic features from CT-images, construction of correlation matrices, and selection of criteria for classification. The results show that the obtained radiomic parameters have a significant correlation with the presence of sarcopenia, allowing the construction of accurate classification models based on machine learning. This approach can significantly improve the diagnosis of sarcopenia, providing reliable non-invasive analysis methods.
Keywords: radiomics, texture analysis, machine learning, sarcopenia. 
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     title = {Applying radiomics in computed tomography data analysis to predict sarcopenia},
     journal = {Vestnik Sankt-Peterburgskogo universiteta. Prikladna\^a matematika, informatika, processy upravleni\^a},
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     publisher = {mathdoc},
     volume = {20},
     number = {3},
     year = {2024},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/VSPUI_2024_20_3_a5/}
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I. A. Schmidt; E. D. Kotina. Applying radiomics in computed tomography data analysis to predict sarcopenia. Vestnik Sankt-Peterburgskogo universiteta. Prikladnaâ matematika, informatika, processy upravleniâ, Tome 20 (2024) no. 3, pp. 376-390. http://geodesic.mathdoc.fr/item/VSPUI_2024_20_3_a5/