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Predictive models for sedation and analgesia scales using artificial intelligence.

Research Project

Project/Area Number 18K08896
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeMulti-year Fund
Section一般
Review Section Basic Section 55060:Emergency medicine-related
Research InstitutionYokohama City University

Principal Investigator

YOKOSE Masashi  横浜市立大学, 附属病院, 講師 (70614402)

Co-Investigator(Kenkyū-buntansha) 高木 俊介  横浜市立大学, 附属病院, 准教授 (90644823)
Project Period (FY) 2018-04-01 – 2024-03-31
Project Status Completed (Fiscal Year 2023)
Budget Amount *help
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2021: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2020: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Keywords人工知能 / 表情認識 / 遠隔集中治療 / 鎮静スケール
Outline of Final Research Achievements

The purpose of this study was to create a model for the continuous calculation of the sedation scale based on facial images of patients' expressions acquired by a camera installed in the ceiling of a hospital room. The goal was to develop a model to discriminate between the two groups of A and VPU in the AVPU score [Alert (clear consciousness) Verbal (responsive to voice prompts) Pain (responsive to pain stimuli) Unresponsive (no response)]. The agreement of the model to discriminate the two groups, A and VPU, based on the percentage of eyes open and closed, with the discrimination by health care providers was approximately 50-80%. Based on these results, future projects will be directed toward implementation of the model in real-world clinical practice.

Academic Significance and Societal Importance of the Research Achievements

高い精度を持つ開閉眼予測モデルの作成が達成された。また、意識レベル評価スケールであるAVPUにおけるAとVPUとを識別するモデルについても比較的高い精度を持ったモデルの作成が達成された。当初はRichmond Agitation-Sedation Scale等のより一般的なスケール評価が目標であったが、体動の検出などより高度なモデルの作成が必要であった。今後は、体動の検知モデルの開発やバイタルサインのトレンドを評価に加える等、モデル精度を発展的に高めることが目標となる。開閉眼評価は一般的な鎮静・意識スケールに含まれる項目であり、本研究はこれらの評価モデル開発の足掛かりとしての意義があった。

Report

(7 results)
  • 2023 Annual Research Report   Final Research Report ( PDF )
  • 2022 Research-status Report
  • 2021 Research-status Report
  • 2020 Research-status Report
  • 2019 Research-status Report
  • 2018 Research-status Report
  • Research Products

    (6 results)

All 2024 2022 2021 2020 2019

All Presentation (6 results) (of which Int'l Joint Research: 2 results)

  • [Presentation] 患者画像解析を用いたAIモデル構築と現場実装における課題2024

    • Author(s)
      髙木俊介,横瀬真志,田端 篤,中西 彰
    • Organizer
      第51回日本集中治療医学会学術
    • Related Report
      2023 Annual Research Report
  • [Presentation] 目の状態に基づく意識状態モニタリングの現状と課題2022

    • Author(s)
      青山祥太朗, 南部雄磨, 田端篤, 辻杏歩, 高木俊介
    • Organizer
      第33回日本臨床モニター学会総会
    • Related Report
      2022 Research-status Report
  • [Presentation] モニタリングカメラの映像解析による患者重症度推定の可能性2022

    • Author(s)
      南部雄磨, 田端篤, 青山祥太朗, 辻杏歩, 飯田裕太, 長田光平, 山本浩平, 髙木俊介
    • Organizer
      第26回日本遠隔医療学会学術大会
    • Related Report
      2022 Research-status Report
  • [Presentation] 画像解析による意識レベルモニタリングの挑戦と限界2021

    • Author(s)
      田端 篤,高木 俊介,南部 雄磨,東島 紋子,辻 杏歩,青山 祥太朗
    • Organizer
      第25回日本遠隔医療学会学術大会
    • Related Report
      2021 Research-status Report
  • [Presentation] Comparison between open source and trained neural network model in face detection for critical ill patients.2020

    • Author(s)
      Akane Sato, Shunsuke Takaki, Masashi Yokose, Takahisa Goto
    • Organizer
      Euroanaesthesia 2020
    • Related Report
      2020 Research-status Report
    • Int'l Joint Research
  • [Presentation] Automatic assessment model with facial recognition and machine learning of patient’s image in ICU patients.2019

    • Author(s)
      Akane Sato, Shunsuke Takaki, Masashi Yokose, Takahisa Goto
    • Organizer
      Euroanaesthesia 2019
    • Related Report
      2019 Research-status Report
    • Int'l Joint Research

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Published: 2018-04-23   Modified: 2025-01-30  

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