Surgical Procedure Understanding, Evaluation, and Interpretation: A Dictionary Factorization Approach
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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.
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http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/version/c_970fb48d4fbd8a85
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