術室内映像を用いた潜在的リスク源候補半自動抽出システムの開発 Semi-automatic Potential Risk Factor Candidate Detection using Intraoperative Video Image

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著者

    • 鈴木 孝司 SUZUKI Takashi
    • 東京女子医科大学 先端生命医科学研究所 先端工学外科学分野 Faculty of Advanced Techno Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University
    • 櫻井 康雄 SAKURAI Yasuo
    • 東京女子医科大学 先端生命医科学研究所 先端工学外科学分野 Faculty of Advanced Techno Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University
    • 吉光 喜太郎 [他] YOSHIMITSU Kitaro
    • 東京女子医科大学 先端生命医科学研究所 先端工学外科学分野 Faculty of Advanced Techno Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University
    • 南部 恭二郎 NAMBU Kyojiro
    • 東京女子医科大学 先端生命医科学研究所 先端工学外科学分野 Faculty of Advanced Techno Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University
    • 村垣 善浩 MURAGAKI Yoshihiro
    • 東京女子医科大学 先端生命医科学研究所 先端工学外科学分野 Faculty of Advanced Techno Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University
    • 伊関 洋 ISEKI Hiroshi
    • 東京女子医科大学 先端生命医科学研究所 先端工学外科学分野 Faculty of Advanced Techno Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University

抄録

Medical errors are critical issues in medical practice. Serious accidents are deeply investigated, but most slight cases are ignored as careless mistakes by clinical staff. Identification of potential risks in surgery and treatment of risks are important to decrease errors. Video recording and analyzing system was developed to record intraoperative information and to find risk factors, but visual mining by medical doctors requires much time and effort and the results will be subjective. Thus we adopted quantity of motion in the recorded video as a quantitative index to indicate "candidates" of incidents. This system was evaluated in a clinical case (brain tumor removal) to compare detecting ability of incidents between human observation and computer processing. While human observation took 8.7 hours (equivalent to operative time) and found 4 incidents, computer processing took only 2.7 hours and extracted 81 candidates under tentative extraction threshold. Two events were common to both methods, but results of computation contained many false positive cases and does not detect rest two cases which human observation succeeded. Computer detection reduced the time to find risk factors, but it contained false detection and could not detect motionless incidents. We will integrate other featuring quantity and machine learning methods.

収録刊行物

  • Journal of Japan Society of Computer Aided Surgery : J.JSCAS

    Journal of Japan Society of Computer Aided Surgery : J.JSCAS 13(2), 75-85, 2011-09-30

    The Japan Society of Computer Aided Surgery

参考文献:  12件中 1-12件 を表示

被引用文献:  2件中 1-2件 を表示

各種コード

  • NII論文ID(NAID)
    10030199077
  • NII書誌ID(NCID)
    AA11908088
  • 本文言語コード
    JPN
  • 資料種別
    ART
  • ISSN
    13449486
  • NDL 記事登録ID
    023683029
  • NDL 請求記号
    Z74-B824
  • データ提供元
    CJP書誌  CJP引用  NDL  J-STAGE 
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