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Automatic-diagnosis of panreatic diseases using artifical intelligence

Research Project

Project/Area Number 18K15769
Research Category

Grant-in-Aid for Early-Career Scientists

Allocation TypeMulti-year Fund
Review Section Basic Section 53010:Gastroenterology-related
Research InstitutionAichi Cancer Center Research Institute

Principal Investigator

Takamichi Kuwahara  愛知県がんセンター(研究所), がん予防研究分野, 研究員 (10816408)

Project Period (FY) 2018-04-01 – 2021-03-31
Project Status Completed (Fiscal Year 2020)
Budget Amount *help
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2020: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Keywords人工知能 / deep learning / 膵嚢胞 / IPMN / 膵腫瘍 / 膵管癌 / 膵神経内分泌腫瘍 / 慢性膵炎 / 膵臓 / 超音波内視鏡
Outline of Final Research Achievements

We developed AI system for the diagnosis of pancreatic malignancy which was difficult to be diagnosed by other image analysis. At first, we developed the AI system which diagnosed the IPMN malignancy and used EUS images of 50 IPMN patients. ResNet50 was used for this AI system and was developed by tensorflow and we evaluated this AI system using 10-fold cross validation. Using this AI system, we could diagnose IPMN malignancy (accuracy 94%).
Next, we developed the AI system for the diagnose of pancreatic tumor and used pancreatic ductal carcinoma (PDAC), pancreatic neuroendocrine tumor, autoimmune pancreatitis, and chronic pancreatitis patients (900 patients). Efficientnet-b4 was used for this AI system and developed by pytorch. Using super-computing resources, we evaluated this AI system by external validation. Using this AI system, we could diagnose PDAC or not (accuracy 90%)

Academic Significance and Societal Importance of the Research Achievements

AIによって超音波内視鏡(EUS)画像を解析することで他モダリティでは判定困難な膵疾患を高精度に診断することが可能であることを示した。今後薬事申請を踏まえた研究計画を立て一般的臨床で使用できるようにすることを目指す。それにより膵疾患の治療タイミングを逃さない、または不要な手術を減らすことができるなど膵疾患に対する治療判断の精度を向上することができるようになる。

Report

(4 results)
  • 2020 Annual Research Report   Final Research Report ( PDF )
  • 2019 Research-status Report
  • 2018 Research-status Report
  • Research Products

    (13 results)

All 2021 2020 2019 2018

All Journal Article (3 results) (of which Peer Reviewed: 3 results,  Open Access: 3 results) Presentation (10 results) (of which Invited: 2 results)

  • [Journal Article] Current status of artificial intelligence analysis for endoscopic ultrasonography2021

    • Author(s)
      Kuwahara T, Hara K, Mizuno N, et al
    • Journal Title

      Dig Endosc

      Volume: 33 Issue: 2 Pages: 298-305

    • DOI

      10.1111/den.13880

    • Related Report
      2020 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Usefulness of Deep Learning Analysis for the Diagnosis of Malignancy in Intraductal Papillary Mucinous Neoplasms of the Pancreas2019

    • Author(s)
      Kuwahara T, Hara K, Mizuno N, et al.
    • Journal Title

      Clinical and Translational Gastroenterology

      Volume: 印刷中 Issue: 5 Pages: e00045-e00045

    • DOI

      10.14309/ctg.0000000000000045

    • Related Report
      2018 Research-status Report
    • Peer Reviewed / Open Access
  • [Journal Article] Diagnostic ability of artificial intelligence using deep learning analysis of cyst fluid in differentiating malignant from benign pancreatic cystic lesions2019

    • Author(s)
      Kurita Y, Kuwahara T, Hara K, et al.
    • Journal Title

      Scientific reports

      Volume: - Issue: 1

    • DOI

      10.1038/s41598-019-43314-3

    • Related Report
      2018 Research-status Report
    • Peer Reviewed / Open Access
  • [Presentation] 超音波内視鏡画像を用いた膵疾患に対するAI診断の取り組み2021

    • Author(s)
      桑原 崇通
    • Organizer
      日本消化器病学会 第3回ビッグデータ・AI研究会
    • Related Report
      2020 Annual Research Report
    • Invited
  • [Presentation] Deep learningを用いた膵腫瘍良悪性診断2021

    • Author(s)
      桑原崇通, 原和生, 清水泰博
    • Organizer
      第51回日本膵臓学会大会
    • Related Report
      2020 Annual Research Report
  • [Presentation] Usefulness of Deep Learning Analysis for the Diagnosis of Malignancy in IPMN.2020

    • Author(s)
      Kuwahara T, Hara K, Shimizu Y.
    • Organizer
      Japanese Digestive Disease Week (JDDW)2020
    • Related Report
      2020 Annual Research Report
    • Invited
  • [Presentation] 膵疾患診断AIの有効性と学習効率化への取り組み2020

    • Author(s)
      桑原崇通、清水泰博、水野伸匡
    • Organizer
      Japanese Digestive Disease Week (JDDW)2020
    • Related Report
      2020 Annual Research Report
  • [Presentation] 人工知能を用いた膵疾患診断の有用性2020

    • Author(s)
      桑原崇通, 原和生, 清水泰博
    • Organizer
      第99回日本消化器内視鏡学会総会
    • Related Report
      2020 Annual Research Report
  • [Presentation] AIを用いたIPMN良悪性診断2019

    • Author(s)
      桑原崇通、原和生、丹羽康正
    • Organizer
      第97回日本消化器内視鏡学会総会
    • Related Report
      2019 Research-status Report
  • [Presentation] 人工知能を用いたIPMN良悪性診断の試み2019

    • Author(s)
      桑原崇通、原和生、丹羽康正
    • Organizer
      第105回日本消化器病学会総会
    • Related Report
      2019 Research-status Report
  • [Presentation] Deep learningを用いたIPMN良悪性診断2019

    • Author(s)
      桑原崇通 奥野のぞみ 原和生
    • Organizer
      JDDW2019
    • Related Report
      2019 Research-status Report
  • [Presentation] AIを用いたIPMN良悪性診断の試み2019

    • Author(s)
      桑原崇通 水野伸匡 原和生
    • Organizer
      第105回日本消化器病学会総会
    • Related Report
      2018 Research-status Report
  • [Presentation] 嚢胞液解析を用いた膵嚢胞診断の現状とニューラルネットワークを用いた診断能向上の試み2018

    • Author(s)
      栗田裕介 桑原崇通 原和生
    • Organizer
      第104回日本消化器病学会総会
    • Related Report
      2018 Research-status Report

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Published: 2018-04-23   Modified: 2022-01-27  

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