Imaging studies of neurodegeneration by deep learning
| Project/Area Number |
18K18452
|
|---|
| Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
|
| Allocation Type | Multi-year Fund |
|---|
| Review Section |
Studies on the Super-Aging Society
|
|---|
| Research Institution | Kyoto University |
|---|
Principal Investigator |
Haruhisa Inoue 京都大学, iPS細胞研究所, 教授 (70332327)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
近藤 孝之 京都大学, iPS細胞研究所, 特定拠点講師 (80536566)
矢田 祐一郎 国立研究開発法人理化学研究所, バイオリソース研究センター, 特別研究員 (80805797)
|
|---|
| Project Period (FY) |
2018-06-29 – 2021-03-31
|
| Project Status |
Completed (Fiscal Year 2020)
|
| Budget Amount *help |
¥6,370,000 (Direct Cost: ¥4,900,000、Indirect Cost: ¥1,470,000)
Fiscal Year 2020: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2019: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2018: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
|
|---|
| Keywords | ALS / Deep Learning / 超早期病態 / 診断 / データサイエンス / Deep learning / deep learning / 神経変性 / 超高齢社会 / アルツハイマー病 / iPS細胞 |
|---|
| Outline of Final Research Achievements |
In neurodegenerative diseases including ALS, it is necessary to detect and elucidate the pathogenesis of very early lesions. In this study, we generated and imaged motor neurons using iPS cells from healthy subjects and ALS patients, and collected a copious amount of image information. This information was trained by deep learning, which can analyze thousands of dimensions, to identify very early pathological changes that cannot be detected by conventional analysis, and to obtain an index to predict neurodegeneration. Deep learning detected lesions in ALS motor neurons that were undetectable by conventional analysis. The results suggest that deep learning can support the diagnosis of ALS and that this method may help to promote ALS treatment in the future.
|
| Academic Significance and Societal Importance of the Research Achievements |
本研究では、人工知能（Artificial Intelligence: AI）を用いて、神経難病である筋萎縮性側索硬化症（ALS）の病態の検知・診断への応用を目指した研究を行いました。
本研究では、人間の考えを挟むことなく大量のデータからその特徴を抽出できるDeep LearningというAIとiPS細胞由来運動神経細胞を用いて、ALSを予測するAIモデルを構築しました。本研究成果は、今後のAIとiPS細胞のテクノロジー融合による疾患予測と克服に貢献することが期待されます。
|
Report
(4 results)
Research Products
(52 results)
-
-
-
[Journal Article] MicroRNA-33 maintains adaptive thermogenesis via enhanced sympathetic nerve activity.2021
Author(s)
Horie T, Nakao T, Miyasaka Y, Nishino T, Matsumura S, Nakazeki F, Ide Y, Kimura M, Tsuji S, Ruiz Rodriguez R, WatanabeT, Yamasaki T, Xu S, Otani C, Miyagawa S, Matsushita K, Sowa N, Omori A, Tanaka J, Nishimura C, Picciotto MR, Inoue H, Watanabe D, Nakamura K, Sasaki T, Kimura T, Ono K, et al.
-
Journal Title
Nat Commun.
Volume: 12
Issue: 1
Pages: 843-843
DOI
NAID
Related Report
Peer Reviewed / Open Access / Int'l Joint Research
-
-
-
-
[Journal Article] Prediction of compound bioactivities using heat-diffusion equation2020
Author(s)
T. Hidaka, K. Imamura, T. Hioki, T. Takagi, Y. Giga, M.-H. Giga, Y. Nishimura, Y. Kawahara, S. Hayashi, T. Niki, M. Fushimi and H. Inoue
-
Journal Title
Patterns
Volume: 1
Issue: 9
Pages: 100140-100140
DOI
Related Report
Peer Reviewed / Open Access
-
-
-
-
-
-
-
-
-
-
-
-
-
-
[Presentation] Generation of a human induced pluripotent stem cell line derived from a Parkinson’s disease patient with SNCA duplication2021
Author(s)
Hidefumi Suzukia, Naohiro Egawa, Takayuki Kondo, Keiko Imamuraa, Takako Enami, Kayoko Tsukita, Mika Suga, Ran Shibukawa, Yasue Okanishi, Tsuyoshi Uchiyama, Haruhisa Inoue, Ryosuke Takahashi
Organizer
第14回パーキンソン病・運動障害疾患コングレス
Related Report
Invited
-
-
-
-
-
-
-
-
[Presentation] Generation of a human induced pluripotent stem cell line derived from a Parkinson’s disease patient demonstrating SNCA duplication（ポスター発表）2020
Author(s)
Hidefumi Suzukia, Naohiro Egawa, Takayuki Kondo, Keiko Imamuraa, Takako Enami, Kayoko Tsukita, Mika Suga, Ran Shibukawa, Yasue Okanishi, Tsuyoshi Uchiyama, Haruhisa Inoue, Ryosuke Takahashi
Organizer
第43回日本神経科学大会
Related Report
Int'l Joint Research
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
