A Domain-Adapted Machine Learning Approach for Visual Evaluation and Interpretation of Robot-Assisted Surgery Skills
Why this work is in the frame
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Bibliographic record
Abstract
In this study, we present an intuitive machine learning-based 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{1.5}$</tex-math></inline-formula> A Haptic Device. This approach utilized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{t}\text{-}{\mathbf {SNE}}$</tex-math></inline-formula> 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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it