A Domain-Adapted Machine Learning Approach for Visual Evaluation and Interpretation of Robot-Assisted Surgery Skills

  • Author(s) / Creator(s)
  • In this study, we present an intuitive machine learningbased
    approach to evaluate and interpret surgical skills level of a
    participant working with robotic platforms. The proposed method is
    domain-adapted, i.e., jointly utilizes an end-to-end learning approach
    for smoothness detection and domain knowledge-based metrics such
    as fluidity and economy of motion for extracting skills-related features
    within a given trajectory. An advantage of our approach compared
    to similar stochastic or deep learning models is its intuitive and
    transparent manner for extraction and visualization of skills-related
    features within the data. We illustrate the performance of our
    proposed method on trials of the JIGSAWS data set as well as our
    own experimental data gathered from Phantom Premium 1.5A
    Haptic Device. This approach utilized t-SNE technique and provides
    visualized low-dimensional representation for different trials that
    highlights nuanced information within the executive task and returns
    unusual or faulty trials as outliers far away from their normal skill
    or participant clusters. This information regarding the input trajectory
    can be used for evaluation and education applications such as learning
    curve analysis in surgical assessment and training programs.

  • Date created
  • Subjects / Keywords
  • Type of Item
    Article (Published)
  • DOI
  • License
    Attribution-NonCommercial 4.0 International