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ID 116477
タイトル別表記
Deep Learning and ALS
著者
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
資料タイプ
学術雑誌論文
抄録
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.
掲載誌名
Annals of Neurology
ISSN
15318249
cat書誌ID
AA00532923
出版者
American Neurological Association|Wiley Periodicals
89
6
開始ページ
1226
終了ページ
1233
発行日
2021-02-09
権利情報
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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出版社版DOI
出版社版URL
フルテキストファイル
言語
eng
著者版フラグ
出版社版
部局
病院