Surgical Procedure Understanding, Evaluation, and Interpretation: A Dictionary Factorization Approach
Bibliographic record
Abstract
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 the most 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".