Surgical Skill Evaluation From Robot-Assisted Surgery Recordings

  • Author(s) / Creator(s)
  • Quality and safety are critical elements in the
    performance of surgeries. Therefore, surgical trainees need to
    obtain the required degrees of expertise before operating on
    patients. Conventionally, a trainee’s performance is evaluated
    by qualitative methods that are time-consuming and prone to
    bias. Using autonomous and quantitative surgical skill assessment
    improves the consistency, repeatability, and reliability
    of the evaluation. To this end, this paper proposes a videobased
    deep learning framework for surgical skill assessment.
    By incorporating prior knowledge on surgeon’s activity in
    the system design, we decompose the complex task of spatiotemporal
    representation learning from video recordings into
    two independent, relatively-simple learning processes, which
    greatly reduces the model size. We evaluate the proposed
    framework using the publicly available JIGSAWS robotic
    surgery dataset and demonstrate its capability to learn the
    underlying features of surgical maneuvers and the dynamic
    interplay between sequences of actions effectively. The skill
    level classification accuracy of 97:27% on the public dataset
    demonstrates the superiority of the proposed model over prior
    video-based skill assessment methods.

  • Date created
  • Subjects / Keywords
  • Type of Item
    Conference/Workshop Presentation
  • DOI
  • License
    Attribution-NonCommercial 4.0 International