title: Applying radiomics in computed tomography data analysis to predict sarcopenia
metadata.name: dc.contributor.author
metadata.value: Schmidt, Ian A.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Kotina, Elena D.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2024-11-22T17:14:50Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2024-11-22T17:14:50Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2024-09
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Schmidt I. A., Kotina E. D. Applying radiomics in computed tomography data analysis to predict sarcopenia. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 2024, vol. 20, iss. 3, pp. 376–390. https://doi.org/10.21638/spbu10.2024.306 (In Russian)
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/spbu10.2024.306
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/47983
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: 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.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Applied Mathematics. Computer Science. Control Processes;Volume 20; Issue 3
metadata.lang: -
metadata.name: dc.subject
metadata.value: radiomics
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: texture analysis
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: machine learning
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: sarcopenia
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Applying radiomics in computed tomography data analysis to predict sarcopenia
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: vestnik_spbu10_2024_376.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/47983/1/vestnik_spbu10_2024_376.pdf
resources.format: Adobe PDF
resources.size: 1,31 MB