Deep Neural Skill Assessment and Transfer: Application to Robotic Surgery Training
Why this work is in the frame
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Bibliographic record
Abstract
Due to the high sensitivity and complexity of robotic surgery tasks, acquiring appropriate skill levels by trainee surgeons through an effective training process is very important and affects the patient’s safety and the quality of surgical outcomes. With the advanced deep learning technology and the recent availability of surgical procedures data, intelligent methods can be deployed to assess and transfer the skills of an experienced surgeon (mentor) to a novice surgeon (trainee). In this paper, we introduce a novel deep-learning-based skill transfer scheme consisting of a deep convolutional model, SkillNet, and a skill transfer algorithm for robotic surgery training. The proposed SkillNet extracts skill-related features of the mentor from different layers of the network. Then, trainee’s maneuver is enhanced by the proposed skill transfer algorithm while minimizing deviations from the trainee’s original intended trajectory. For validation, the JIGSAWS dataset and also our own experimental data were used to prove the generalizability of SkillNet in capturing skill-related features. The capability of the skill transfer algorithm in enhancing trainee trajectories in terms of predictability, hand tremor reduction, and noise cancellation were investigated separately. The obtained results indicate that this approach can be used as a high-performance filter that makes minor corrections to the input trajectory and improves the skill level of the trainee’s trajectory in practice.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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