Development of beyond human-level AI for medical image diagnosis systems
| Project/Area Number |
18K19892
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| Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
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| Allocation Type | Multi-year Fund |
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| Review Section |
Medium-sized Section 90:Biomedical engineering and related fields
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| Research Institution | Tohoku University |
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Principal Investigator |
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| Project Period (FY) |
2018-06-29 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥6,240,000 (Direct Cost: ¥4,800,000、Indirect Cost: ¥1,440,000)
Fiscal Year 2020: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2019: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
Fiscal Year 2018: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
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| Keywords | 計算機支援診断システム / 人工知能支援診断 / 乳がん / 乳房X線撮影 |
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| Outline of Final Research Achievements |
In this research, next generation medical image diagnosis systems have been developed by using a deep learning based artificial intelligence (AI) that is capable of super-performance beyond human experts. The AI-aided (AID) systems have been applied for breast cancer screening using mammography exmas, gastric cancer screening using fluoroscopic stomac exams, and drowning diagnosis using forensic imaging (autopsy imaging) of x-ray computed tomography. Experimental results showed that the AID systems were able to achieve the human experts' level performance for the tasks. Specifically, the AID system for mammographic diagnosis demonstrated superior performance beyond human experts and further more, the system made human experts' performance even better. These results clearly demonstrated the usefulness and effectiveness of the proposed AID systems in clinical use.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究で開発したAIDシステムは、臨床上十分有用な高性能を達成した。このような医師の診療業務を支援、さらには一部を代替可能な高性能AIの実用化により、医師の業務量低減や非専門領域に対する支援、さらには遠隔医療などを含めた効率化を実現することが可能になり、地方における医師不足に起因する医療提供の持続可能性や、都市部に比して専門医偏在に起因する医療の均てん化問題などの改善に繋がると期待される。
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Report
(5 results)
Research Products
(36 results)
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[Presentation] A Deep Learning Aided Drowning Diagnosis for Forensic Investigations Using Post-Mortem Lung CT Images2020
Author(s)
Noriyasu Homma, Xiaoyong Zhang, Amber Habib Qureshi, Takuya Konno, Yusuke Kawasumi, Akihito Usui, Masato Funayama, Ivo Bukovsky, Kei Ichiji, Norihiro Sugita, Makoto Yoshizawa
Organizer
42nd Annual International Conference of IEEE Engineering in Medicine and Biology Society
Related Report
Int'l Joint Research
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