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Automatic scoliosis angle measurement using deep learning methods, how far we are from clinical application: A narrative review. / Кассаб, Дима Халед Ибрагим; Камышанская, Ирина Григорьевна; Першин, Андрей .

в: Вестник Санкт-Петербургского государственного университета. Серия 11. Медицина, Том 16, № 2, 16.06.2021, стр. 85-94.

Результаты исследований: Научные публикации в периодических изданияхстатьяРецензирование

Harvard

Кассаб, ДХИ, Камышанская, ИГ & Першин, А 2021, 'Automatic scoliosis angle measurement using deep learning methods, how far we are from clinical application: A narrative review', Вестник Санкт-Петербургского государственного университета. Серия 11. Медицина, Том. 16, № 2, стр. 85-94.

APA

Кассаб, Д. Х. И., Камышанская, И. Г., & Першин, А. (Принято в печать). Automatic scoliosis angle measurement using deep learning methods, how far we are from clinical application: A narrative review. Вестник Санкт-Петербургского государственного университета. Серия 11. Медицина, 16(2), 85-94.

Vancouver

Кассаб ДХИ, Камышанская ИГ, Першин А. Automatic scoliosis angle measurement using deep learning methods, how far we are from clinical application: A narrative review. Вестник Санкт-Петербургского государственного университета. Серия 11. Медицина. 2021 Июнь 16;16(2):85-94.

Author

Кассаб, Дима Халед Ибрагим ; Камышанская, Ирина Григорьевна ; Першин, Андрей . / Automatic scoliosis angle measurement using deep learning methods, how far we are from clinical application: A narrative review. в: Вестник Санкт-Петербургского государственного университета. Серия 11. Медицина. 2021 ; Том 16, № 2. стр. 85-94.

BibTeX

@article{73a6ff7bed314686b2a3a685c7c2ca74,
title = "Automatic scoliosis angle measurement using deep learning methods, how far we are from clinical application: A narrative review",
abstract = "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.Key words: scoliosis, automated Сobb angle, artificial neural network (ANN), deep learning (DL).",
author = "Кассаб, {Дима Халед Ибрагим} and Камышанская, {Ирина Григорьевна} and Андрей Першин",
note = "Кассаб, Д., Камышанская, И., & Першин, А. (2021). Automatic scoliosis angle measurement using deep learning methods, how far we are from clinical application: A narrative review. Вестник Санкт-Петербургского университета. Медицина, 16(2), 85–94. https://doi.org/10.21638/spbu11.2021.202 ",
year = "2021",
month = jun,
day = "16",
language = "English",
volume = "16",
pages = "85--94",
journal = " ВЕСТНИК САНКТ-ПЕТЕРБУРГСКОГО УНИВЕРСИТЕТА. МЕДИЦИНА",
issn = "1818-2909",
publisher = "Издательство Санкт-Петербургского университета",
number = "2",

}

RIS

TY - JOUR

T1 - Automatic scoliosis angle measurement using deep learning methods, how far we are from clinical application: A narrative review

AU - Кассаб, Дима Халед Ибрагим

AU - Камышанская, Ирина Григорьевна

AU - Першин, Андрей

N1 - Кассаб, Д., Камышанская, И., & Першин, А. (2021). Automatic scoliosis angle measurement using deep learning methods, how far we are from clinical application: A narrative review. Вестник Санкт-Петербургского университета. Медицина, 16(2), 85–94. https://doi.org/10.21638/spbu11.2021.202

PY - 2021/6/16

Y1 - 2021/6/16

N2 - 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.Key words: scoliosis, automated Сobb angle, artificial neural network (ANN), deep learning (DL).

AB - 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.Key words: scoliosis, automated Сobb angle, artificial neural network (ANN), deep learning (DL).

UR - https://medicine-journal.spbu.ru/article/view/10793

M3 - Article

VL - 16

SP - 85

EP - 94

JO - ВЕСТНИК САНКТ-ПЕТЕРБУРГСКОГО УНИВЕРСИТЕТА. МЕДИЦИНА

JF - ВЕСТНИК САНКТ-ПЕТЕРБУРГСКОГО УНИВЕРСИТЕТА. МЕДИЦИНА

SN - 1818-2909

IS - 2

ER -

ID: 85100115