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Study of interpretation process and development of learning methodology in medical image interpretation skills using artificial intelligence technology

Research Project

Project/Area Number 18K09951
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeMulti-year Fund
Section一般
Review Section Basic Section 58010:Medical management and medical sociology-related
Research InstitutionGunma Prefectural College of Health Sciences

Principal Investigator

Terashita Takayoshi  群馬県立県民健康科学大学, 診療放射線学部, 准教授 (30506241)

Project Period (FY) 2018-04-01 – 2022-03-31
Project Status Completed (Fiscal Year 2021)
Budget Amount *help
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2020: ¥390,000 (Direct Cost: ¥300,000、Indirect Cost: ¥90,000)
Fiscal Year 2019: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2018: ¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Keywords視線解析 / 医用画像読影 / 医用画像読影評価システム / 仮想X線撮影システム / 視線分析 / 骨格X線撮影 / 上部消化管検査 / 深層学習 / ROC解析 / 視線追跡システム / 読影プロセス / 視線追跡 / ディープラーニング / 人工知能
Outline of Final Research Achievements

This study aimed to clarify the differences between skilled and novice users based on gaze data during medical image interpretation and to develop a new training method. Although it was difficult to proceed with the originally planned research due to the outbreak of a new coronavirus during the study period, we were able to accomplish the "development of a medical image interpretation evaluation system incorporating gaze analysis" and the "development of a virtual radiography system" in this study. In the medical image interpretation evaluation system, the use of gaze analysis enabled evaluation based on process rather than outcomes. Because the virtual radiography system can simulate radiographic examinations without using X-rays, it is valuable not only as an evaluation tool but also as a training tool.

Academic Significance and Societal Importance of the Research Achievements

医療行為に関する手技はどれも高度で専門的である.熟練するには経験が必要とされてきたが,これまで「できた」や「できなかった」のアウトカムに基づく教育だったため,不適切な指導により,多くの時間を要していたのではないかと思われる.これをプロセスに基づくことで,効果的で効率的な教育になると予想される.本研究では,高度な医療手技として医用画像読影に焦点を絞り,視線計測による読影中の行動から,熟練者と初学者の違いを明らかにし,新たな訓練法の開発を目指した.新型コロナウィルス感染症のため当初計画通りには進まなかったが,読影能力を評価するための2つのシステムを開発し,今後継続した調査を行うことを可能とした.

Report

(5 results)
  • 2021 Annual Research Report   Final Research Report ( PDF )
  • 2020 Research-status Report
  • 2019 Research-status Report
  • 2018 Research-status Report
  • Research Products

    (12 results)

All 2021 2020 2019 2018

All Journal Article (1 results) (of which Peer Reviewed: 1 results) Presentation (11 results) (of which Int'l Joint Research: 4 results)

  • [Journal Article] Development of an application software for evaluating skills of medical image interpretation using an eye-tracking technique2021

    • Author(s)
      寺下 貴美、佐藤 哲大、小倉 敏裕、土井 邦雄
    • Journal Title

      The Transactions of Human Interface Society

      Volume: 23 Issue: 1 Pages: 43-46

    • DOI

      10.11184/his.23.1_43

    • NAID

      130007991301

    • ISSN
      1344-7262, 2186-8271
    • Year and Date
      2021-02-25
    • Related Report
      2020 Research-status Report
    • Peer Reviewed
  • [Presentation] マーカーレス拡張現実を用いた仮想X線透視アプリケーションの開発2021

    • Author(s)
      寺下貴美,及川憩人,小澤颯,佐藤哲大,小倉敏裕
    • Organizer
      第41回医療情報学連合大会(名古屋)
    • Related Report
      2021 Annual Research Report
  • [Presentation] 上部消化管造影検査のためのバーチャルトレーニングシステムの開発2021

    • Author(s)
      小澤颯,及川憩人,寺下貴美
    • Organizer
      第41回医療情報学連合大会(名古屋)
    • Related Report
      2021 Annual Research Report
  • [Presentation] Open data validation of a classification method of eye movement by a convolutional neural network2021

    • Author(s)
      Kamikawa S, Sato T, Terashita T, Kanaya S.
    • Organizer
      International Forum on Medical Imaging in Asia (IFMIA) 2021 (Taipei, Taiwan (Online))
    • Related Report
      2020 Research-status Report
    • Int'l Joint Research
  • [Presentation] Comparing convolutional neural networks for medical image interpretation based on eye movement analysis2020

    • Author(s)
      Kamikawa S, Terashita T, Kanaya S, Sato T
    • Organizer
      European Congress of Radiology 2020 (Online)
    • Related Report
      2020 Research-status Report
    • Int'l Joint Research
  • [Presentation] Effects of the sampling frequency change in eye movement analysis using deep convolutional neural network: Comparison with other analyses by open annotated gaze data2020

    • Author(s)
      Terashita T, Sato T, Tsutsumi S, Sato M, Ogura T, and Doi k.
    • Organizer
      Asia Pacific Association for Medical Informatics 2020 (Hamamatsu, Japan)
    • Related Report
      2020 Research-status Report
    • Int'l Joint Research
  • [Presentation] Comparing convolutional neural networks for eye movement analysis in medical image interpretation2019

    • Author(s)
      Kamikawa S, Terashita T, Kanaya S, Sato T
    • Organizer
      生体医工学シンポジウム2019(徳島)
    • Related Report
      2019 Research-status Report
  • [Presentation] 深層畳み込みニューラルネットワークを用いた医用画像読影評価システムの開発2019

    • Author(s)
      寺下貴美,佐藤哲大,堤翔子,佐藤充,小倉敏裕,土井邦雄
    • Organizer
      第39回医療情報学連合大会(千葉)
    • Related Report
      2019 Research-status Report
  • [Presentation] Eye movement analysis using deep learning for medical image interpretation2019

    • Author(s)
      Terashita T., Sato T., Tsutsumi S., Sato M., Doi K., Ogura T.
    • Organizer
      European Congress of Radiology 2019 (Vienna, Austria)
    • Related Report
      2018 Research-status Report
    • Int'l Joint Research
  • [Presentation] 教師なし学習を用いた医用画像読影時の視線行動の分類2019

    • Author(s)
      神川怜史、佐藤哲大、寺下貴美、江口遼平、金谷重彦
    • Organizer
      メディカルイメージング連合フォーラム2018(沖縄)
    • Related Report
      2018 Research-status Report
  • [Presentation] Classification of eye movements on a lesion in medical image interpretation using deep learning2018

    • Author(s)
      寺下貴美、堤翔子、佐藤充、土井邦雄、小倉敏裕
    • Organizer
      第57回日本生体医工学会(札幌)
    • Related Report
      2018 Research-status Report
  • [Presentation] Analysis of Eye-tracking in Reading of Medical Images by Use Deep Learning2018

    • Author(s)
      堤翔子、寺下貴美、佐藤充、土井邦雄、小倉敏裕
    • Organizer
      第57回日本生体医工学会(札幌)
    • Related Report
      2018 Research-status Report

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Published: 2018-04-23   Modified: 2023-01-30  

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