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ID 116477
Title Alternative
Deep Learning and ALS
Author
Imamura, Keiko RIKEN|Kyoto University
Yada, Yuichiro Kyoto University|RIKEN
Morita, Mitsuya Jichi Medical University
Kawata, Akihiro Tokyo Metropolitan Neurological Hospital
Arisato, Takayo National Hospital Organization Minamikyusyu Hospital
Nagahashi, Ayako RIKEN|Kyoto University
Enami, Takako RIKEN|Kyoto University
Tsukita, Kayoko Kyoto University|RIKEN
Kawakami, Hideshi Hiroshima University
Nakagawa, Masanori Kyoto Prefectural University of Medicine
Takahashi, Ryosuke Kyoto University
Inoue, Haruhisa RIKEN|Kyoto University
Content Type
Journal Article
Description
In amyotrophic lateral sclerosis (ALS), early diagnosis is essential for both current and potential treatments. To find a supportive approach for the diagnosis, we constructed an artificial intelligence-based prediction model of ALS using induced pluripotent stem cells (iPSCs). Images of spinal motor neurons derived from healthy control subject and ALS patient iPSCs were analyzed by a convolutional neural network, and the algorithm achieved an area under the curve of 0.97 for classifying healthy control and ALS. This prediction model by deep learning algorithm with iPSC technology could support the diagnosis and may provide proactive treatment of ALS through future prospective research.
Journal Title
Annals of Neurology
ISSN
15318249
NCID
AA00532923
Publisher
American Neurological Association|Wiley Periodicals
Volume
89
Issue
6
Start Page
1226
End Page
1233
Published Date
2021-02-09
Rights
This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License(https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
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DOI (Published Version)
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language
eng
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departments
University Hospital