A Deep Learning Classification Model for Predicting Brain Atrophy in Multiple Sclerosis

被引：0
|
作者：
Zhan, Geng [1 ]
Wang, Dongang [1 ]
Cabezas, Mariano [1 ]
Beadnall, Heidi [2 ]
Kyle, Kain [1 ]
Ly, Linda [3 ]
Kalincik, Tomas [4 ]
Barnett, Michael [1 ]
Wang, Chenyu [1 ]
机构：
[1] Univ Sydney, Sydney, NSW, Australia
[2] Sydney Neurol, Sydney, NSW, Australia
[3] Sydney Neuroimaging Anal Ctr, Sydney, NSW, Australia
[4] Univ Melbourne, Melbourne, Vic, Australia
来源：
MULTIPLE SCLEROSIS JOURNAL | 2023年 / 29卷 / 07期
关键词：
D O I：
暂无
中图分类号：
R74 [神经病学与精神病学];
学科分类号：
摘要：
P-84
引用
下载
收藏
页码：NP41 / NP41
页数：1
相关论文
共 50 条
  • [31] Atrophy of the brain and spinal cord in multiple sclerosis
    Bakshi, R
    JOURNAL OF NEUROIMAGING, 2004, 14 (03) : 3S - 4S
  • [32] A Hybrid Deep Learning Model for Brain Tumour Classification
    Rasool, Mohammed
    Ismail, Nor Azman
    Boulila, Wadii
    Ammar, Adel
    Samma, Hussein
    Yafooz, Wael M. S.
    Emara, Abdel-Hamid M.
    ENTROPY, 2022, 24 (06)
  • [33] Value brain atrophy and magnetization transfer MRI measures in predicting disability in patients with multiple sclerosis
    Fritz, DA
    Weinstock-Gunman, B
    Hussein, S
    Dwyer, MG
    Tjoa, CW
    Abelrahman, N
    Munschauer, FE
    Garg, N
    Zivadinov, R
    NEUROLOGY, 2006, 66 (05) : A98 - A98
  • [34] An investigation of the effect of brain atrophy on brain injury in multiple sclerosis
    Abdi, Hamed
    Hassani, Kamran
    Shojaei, Shahrokh
    JOURNAL OF THEORETICAL BIOLOGY, 2023, 557
  • [35] Predicting disease course in Multiple Sclerosis using deep learning on clinical data
    Sreenivasan, Akshai Parakkal
    Burman, Joachim
    Kultima, Kim
    Spjuth, Ola
    Vaivade, Aina
    MULTIPLE SCLEROSIS JOURNAL, 2023, 29 : 22 - 22
  • [36] Brain Age in Multiple Sclerosis: A comparison of traditional machine learning and deep learning methods
    Skattebol, L.
    Stromstad, M.
    Leonardsen, E.
    Kaufmann, T.
    Moridi, T.
    Stawiarz, L.
    Ouellette, R.
    Ineichen, B.
    Ferreira, D.
    Muehlboeck, S.
    Brune, S.
    Nygaard, G.
    Berg-Hansen, P.
    Sowa, P.
    Manouchehrinia, A.
    Westman, E.
    Olsson, T.
    Celius, E.
    Hillert, J.
    Kockum, I.
    Harbo, H.
    Piehl, F.
    Granberg, T.
    Westlye, L.
    Hogestol, E.
    EUROPEAN JOURNAL OF NEUROLOGY, 2022, 29 : 144 - 145
  • [37] Brain age in multiple sclerosis: a comparison between machine learning and deep learning models
    Skattebol, L.
    Stromstad, M.
    Leonardsen, E. H.
    Kaufmann, T.
    Moridi, T.
    Stawiarz, L.
    Ouellette, R.
    Ineichen, B. V.
    Ferreira, D.
    Muehlboeck, S.
    Brune, S.
    Nygaard, G. O.
    Berg-Hansen, P.
    Beyer, M. K.
    Sowa, P.
    Manouchehrinia, A.
    Westman, E.
    Beck, D.
    Olsson, T.
    Celius, E. G.
    Hillert, J.
    Kockum, I.
    Harbo, H. F.
    Piehl, F.
    Granberg, T.
    Westlye, L. T.
    Hogestol, E. A.
    MULTIPLE SCLEROSIS JOURNAL, 2022, 28 (3_SUPPL) : 25 - 26
  • [38] Deep learning with diffusion basis spectrum imaging for classification of multiple sclerosis lesions
    Ye, Zezhong
    George, Ajit
    Wu, Anthony T.
    Niu, Xuan
    Lin, Joshua
    Adusumilli, Gautam
    Naismith, Robert T.
    Cross, Anne H.
    Sun, Peng
    Song, Sheng-Kwei
    ANNALS OF CLINICAL AND TRANSLATIONAL NEUROLOGY, 2020, 7 (05): : 695 - 706
  • [39] Brain atrophy and cognitive impairment in multiple sclerosis: a review
    Michael Lanz
    Horst K. Hahn
    Helmut Hildebrandt
    Journal of Neurology, 2007, 254 : II43 - II48
  • [40] Normalized regional brain atrophy measures in multiple sclerosis
    Locatelli, L
    Zivadinov, R
    Stival, B
    Bratina, A
    Grop, A
    Nasuelli, D
    Brnabic-Razmilic, O
    Zorzon, M
    NEUROLOGY, 2002, 58 (07) : A206 - A206
12345