Kinematics Skill Modeling of Cardiac Catheterization via Deep Learning Method
Bibliographic record
Abstract
According to advances in robotic surgery, the importance of data-driven techniques that incorporate deep learning methods is expanding quickly, with a focus on objective surgical skill evaluation. Unlike traditional evaluation where surgeons’ skills are evaluated in the real surgery room, capturing users’ motion kinematics can be used as input for an AI model to assess their skills. For this study, a simulated mechanical setup has been provided for the trainees, focusing on cardiac catheterization procedures. This setup allows users to engage in hands-on practice while simultaneously capturing their hand movements for further evaluation. Trainees have the opportunity to engage in extensive practice on a mechanical setup as a pre-operation procedure, enabling them to develop a deeper familiarity and understanding. The task is to pass the tip of a commercial catheter through curves and level intersections on a plastic transparent blood vessel phantom. The objective is to guide the catheter’s tip from the vessel entry point to the designated ablation target. By conducting various experiments involving both novices and experts, a deep recurrent neural network was employed to extract a skill model by solving a binary classification task. The trained model demonstrated a remarkable $\mathbf{9 2. 3} \%$ accuracy in effectively discerning between the maneuvers performed by novices and experts, indicating the successful implementation of the proposed methodology.
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How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".