Towards Surgical Skill Modeling in Cardiac Ablation Using Deep Learning
Bibliographic record
Abstract
The most widely practised method for treating cardiovascular problems is minimally invasive surgery (MIS). The rising interest in surgical robots and simulations has led to a greater demand for more objective methods of skill evaluation. Traditionally, the performance of novices is evaluated using surgeons’ skills through a specific and streamlined ablation task. To this end, an experimental setup was proposed to provide a simulated ablation procedure through a mechanical system. It is equipped with the synthetic heartbeat mechanism of the heart with the capability of measuring the contact forces between a catheter’s tip and a force sensor. Using a commercially available catheter for ablation, the task was to maintain the force within a safe range while the tip of the catheter is touching the surface of the sensor. Accomplishing multiple experiments by novices and experts, a deep recurrent neural network was considered to extract the model of skills by solving a binary classification problem. The results of the trained model showed that the proposed pipeline was able to properly distinguish the novices’ from experts’ maneuvers with 95% accuracy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".