Deep learning-based video-analysis of instrument motion in microvascular anastomosis training.

Journal: Acta neurochirurgica
Published Date: Jan 12, 2024

Abstract

PURPOSE: Attaining sufficient microsurgical skills is paramount for neurosurgical trainees. Kinematic analysis of surgical instruments using video offers the potential for an objective assessment of microsurgical proficiency, thereby enhancing surgical training and patient safety. The purposes of this study were to develop a deep-learning-based automated instrument tip-detection algorithm, and to validate its performance in microvascular anastomosis training.

Authors

  • Taku Sugiyama
    Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai, 980-8579, Japan.
  • Hiroyuki Sugimori
    Faculty of Health Sciences, Hokkaido University, Sapporo 060-0812, Japan.
  • Minghui Tang
    Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan.
  • Yasuhiro Ito
    Department of Neurosurgery, Hokkaido University Graduate School of Medicine, North 15 West 7, Kita-Ku, Sapporo, 060-8638, Japan.
  • Masayuki Gekka
    Department of Neurosurgery, Hokkaido University Graduate School of Medicine, North 15 West 7, Kita-Ku, Sapporo, 060-8638, Japan.
  • Haruto Uchino
    Department of Neurosurgery, Hokkaido University Graduate School of Medicine, North 15 West 7, Kita-Ku, Sapporo, 060-8638, Japan.
  • Masaki Ito
    Department of Neurosurgery, Hokkaido University Graduate School of Medicine, North 15 West 7, Kita-Ku, Sapporo, 060-8638, Japan.
  • Katsuhiko Ogasawara
  • Miki Fujimura
    Department of Neurosurgery, Hokkaido University Graduate School of Medicine, North 15 West 7, Kita-Ku, Sapporo, 060-8638, Japan.

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