title: Application of artificial intelligence in the detection and stratification of prostate cancer: Literature review
metadata.name: dc.contributor.author
metadata.value: Talyshinskii, Ali E.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Kamyshanskaya, Irina G.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Mischenko, Andrey V.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Guliev, Bakhman G.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Bakhtiozin, Rustam F.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2023-10-10T13:57:00Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2023-10-10T13:57:00Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2023-06
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Talyshinskii A. E., Kamyshanskaya I. G., Mischenko A. V., Guliev B. G., Bakhtiozin R. F. Application of artificial intelligence in the detection and stratification of prostate cancer: Literature review. Vestnik of Saint Petersburg University. Medicine, 2023, vol. 18, issue 2, pp. 150–166. https://doi.org/10.21638/spbu11.2023.204 (In Russian)
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/spbu11.2023.204
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/44203
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: This review examines the current methodologies employed in utilizing artificial intelligence
  for the identification and classification of prostate cancer using magnetic resonance imaging data. It outlines the volume of data utilized and highlights the most commonly sought-after
  sequences employed for training neural networks. The review further presents the accuracy
  metrics of the neural networks analyzed, accompanied by a succinct explanation of each metric.
  Furthermore, the review pinpoints the limitations associated with contemporary neural
  networks devised for the detection and classification of prostate cancer using magnetic resonance
  imaging data, as well as the challenges encountered during their creation and implementation.
  In summary, this comprehensive analysis delves into the existing approaches in
  leveraging artificial intelligence for prostate cancer detection and stratification through magnetic
  resonance imaging data. It addresses the data scale and preferred magnetic resonance
  imaging sequences employed for neural network training. The review provides a breakdown
  of accuracy indicators for the neural networks evaluated, elucidating their respective capabilities.
  Moreover, the review identifies the drawbacks associated with current neural network
  models developed for prostate cancer detection and stratification via magnetic resonance imaging
  data, while also recognizing the complexities involved in their development and practical
  application.
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. Medicine;Volume 18; Issue 2
metadata.lang: -
metadata.name: dc.subject
metadata.value: prostate cancer
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: MRI
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: artificial intelligence
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Application of artificial intelligence in the detection and stratification of prostate cancer: Literature review
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 04.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/44203/1/04.pdf
resources.format: Adobe PDF
resources.size: 781,05 kB