title: Automatic scoliosis angle measurement using deep learning methods, how far we are from clinical application: A narrative review
metadata.name: dc.contributor.author
metadata.value: Kassab, Dima Kh. I.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Kamyshanskaya, Irina G.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Pershin, Andrei A.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2021-12-15T13:03:45Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2021-12-15T13:03:45Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2021-06
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Kassab D. Kh. I., Kamyshanskaya I. G., Pershin A. A. Automatic scoliosis angle measurement using deep learning methods, how far we are from clinical application: A narrative review. Vestnik of Saint Petersburg University. Medicine, 2021, vol. 16, issue 2, pp. 85–94.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/spbu11.2021.202
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/33944
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: In recent years, automatic measurement of scoliosis angle using deep learning (DL) techniques
  is being studied extensively. The objective of this study is to review and assess the
  clinical applicability of these new methods. A wide search for English and Russian literature
  was conducted, 13 studies were included. Although the results of many of the reviewed DL
  methods in measuring the angle of scoliosis are promising, their clinical implication is by
  far not possible. There is absence of consensus in many issues regarding these new methods
  (differences in architecture of the ANN, data set, principle of angle measurement and nature
  of the reported results). In order to successfully introduce these new methods into clinical
  practice, more comparative and prospective studies are needed. Also, a multidisciplinary team
  including technical and medical workers is needed.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
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 16; Issue 2
metadata.lang: -
metadata.name: dc.subject
metadata.value: scoliosis
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: automated Сobb angle
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: artificial neural network
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: deep learning
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Automatic scoliosis angle measurement using deep learning methods, how far we are from clinical application: A narrative review
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 85-94.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/33944/1/85-94.pdf
resources.format: Adobe PDF
resources.size: 704,33 kB