| Project/Area Number |
17K10447
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Radiation science
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| Research Institution | Nagasaki University |
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Principal Investigator |
KUDO Takashi 長崎大学, 原爆後障害医療研究所, 教授 (20330300)
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| Co-Investigator(Kenkyū-buntansha) |
井手口 怜子 長崎大学, 原爆後障害医療研究所, 助教 (10457567)
西 弘大 長崎大学, 原爆後障害医療研究所, 助教 (10719496)
上谷 雅孝 長崎大学, 医歯薬学総合研究科(医学系), 教授 (40176582)
前村 浩二 長崎大学, 医歯薬学総合研究科(医学系), 教授 (90282649)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2019: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 虚血性心疾患 / 核医学 / 心筋血流シンチグラフィ / 位相解析 / 人工知能 / 心臓核医学 / 心不全 / 定量 / 心筋血流 / 核医学検査 / 虚血評価 / 医療経済 |
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| Outline of Final Research Achievements |
To improve the diagnostic accuracy of myocardial perfusion imaging(MPI) without using expensive equipment, phase analysis and artificial intelligence techniques were applied. Using the phase analysis, it became clear that the change of cardiac phase distribution was observed only in the patient of the severe ischemia. This indicates that phase analysis may contribute to the stratification of the severity of ischemic heart disease. Regarding the application of artificial intelligence, adding diagnosis assistance using artificial intelligence during diagnosis process of MPI improve the accuracy of image interpretation make it close to that of expert interpretation. This results indicates that artificial intelligence may contributed saving the human resources in clinical practices. We also performed basic research regarding improvement of image quality using cimetidine before MPI process.
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| Academic Significance and Societal Importance of the Research Achievements |
医療費の高騰を招くことなく、ソフトウエア、解析手法、安価な薬剤の追加といった経済的負担の少ない方法を追加するのみで、心臓核医学検査の診断精度を向上させることが出来ることを明らかにした。また、人工知能の臨床への応用は、初心者と熟練者の診断精度の差を埋めることに役立つことが明らかとなり、費用の削減効果のみでなく、人的資源の節約・適正配分にも役立つことが明らかとなった。
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