Surgical Procedure Understanding, Evaluation, and Interpretation: A Dictionary Factorization Approach

• Author(s) / Creator(s)

  • Abed Soleymani
  • Xingyu Li
  • Mahdi Tavakoli

• In this study, we present a novel machine learning-based
  technique to help surgical mentors assess surgical motion trajectories
  and corresponding surgical skills levels in surgical training programs.
  The proposed method is a variation of sparse coding and dictionary
  learning that is straightforward to optimize and produces approximate
  trajectory decomposition for structured tasks. Our approach is superior
  to existing stochastic or deep learning-based methods in terms
  of transparency of the model and interpretability of the results. We
  introduce a dual-sparse coding algorithm which encourages the elimination
  of redundant and unnecessary atoms and targets to reach the
  most informative dictionary, representing themost important temporal
  variations within a given surgical trajectory. Since surgical tool trajectories
  are time series signals, we further incorporate the idea of floating
  atoms along the temporal axis in trajectory analysis, which improves
  the model’s accuracy and prevents information loss in downstream
  tasks.Using JIGSAWS data set,we present preliminary results showing
  the feasibility of the proposed method for clustering and interpreting
  surgical trajectories in terms of user’s skills-related behaviors.

• Date created

  2022-01-01

• Subjects / Keywords

  • Machine Learning, Dictionary Learning, Sparse Coding, Surgical Trajectory Decomposition, Surgical Skills Assessment

• Type of Item

  Article (Published)

• DOI

  https://doi.org/10.7939/r3-6shh-be48

• License

  Attribution-NonCommercial 4.0 International