Autonomous countertraction for secure field of view in laparoscopic surgery using deep reinforcement learning.

Journal: International journal of computer assisted radiology and surgery
PMID: 39285110

Abstract

PURPOSE: Countertraction is a vital technique in laparoscopic surgery, stretching the tissue surface for incision and dissection. Due to the technical challenges and frequency of countertraction, autonomous countertraction has the potential to significantly reduce surgeons' workload. Despite several methods proposed for automation, achieving optimal tissue visibility and tension for incision remains unrealized. Therefore, we propose a method for autonomous countertraction that enhances tissue surface planarity and visibility.

Authors

  • Yuriko Iyama
    Graduate School of Engineering, The University of Tokyo, Tokyo, Japan.
  • Yudai Takahashi
    Graduate School of Engineering, The University of Tokyo, Tokyo, Japan.
  • Jiahe Chen
    Graduate School of Engineering, The University of Tokyo, Tokyo, Japan.
  • Takumi Noda
  • Kazuaki Hara
    Graduate School of Engineering, The University of Tokyo, Tokyo, Japan.
  • Etsuko Kobayashi
    Institute of Advanced BioMedical Engineering and Science, Tokyo Women's Medical University, Tokyo, Japan.
  • Ichiro Sakuma
    Department of Precision Engineering, The University of Tokyo, Tokyo, Japan.
  • Naoki Tomii
    Faculty of Engineering, The University of Tokyo, 7-3-1, Bunkyo-ku, 113-8656, Tokyo, Japan. Electronic address: naoki_tomii@bmpe.t.u-tokyo.ac.jp.

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