| Project/Area Number |
18K15565
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 52040:Radiological sciences-related
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| Research Institution | Fujita Health University (2021)
Teikyo University (2018-2020) |
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Principal Investigator |
Shiiba Takuro 藤田医科大学, 医療科学部, 准教授 (30759501)
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| Project Period (FY) |
2018-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2020: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
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| Keywords | パーキンソン病 / 人工知能 / 自動分類 / 非定型パーキンソン症候群 / ドパミントランスポーター / SPECT / 機械学習 / 特徴量 / 画像特徴量 / パーキンソン症候群 / 非典型的パーキンソン症候群 |
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| Outline of Final Research Achievements |
This study aimed to focus on the image features of dopamine transporter (DAT) SPECT, a biofunctional imaging technique, and to develop the next-generation imaging diagnosis method, an automatic classification system for Parkinson's disease (PD) and atypical Parkinson syndrome (APS), by introducing machine learning. However, the organization of APS cases planned initially did not proceed due to the spread of the new coronavirus infection.
Therefore, the objectives were set as constructing a radiomics signature and developing an automated classification system for PD and healthy subjects. As a result of the research, a highly accurate automatic classification system and a radiomics signature for PD was constructed. The results are expected to be applied to APS.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究では、パーキンソン病の高精度な自動分類システムの構築と新たな画像バイオマーカを提案することができた。本研究で構築した自動分類システムの使用によって従来のパーキンソン病の画像診断の正確さを向上させる可能性があり、今後パーキンソン病と非定型パーキンソン症候群への応用が期待できる。
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