On the feasibility of heart motion compensation on the daVinci® surgical robot for coronary artery bypass surgery: Implementation and user studies
Bibliographic record
Abstract
This paper describes the implementation of a heart motion compensation system on the da Vinci surgical system (Intuitive Surgical Inc.) for coronary artery bypass surgery. By introducing a robot-assisted solution, this surgery could be performed completely minimally invasively and on a beating heart. In this work we describe the development of open loop controllers based on spectral line decomposition and the assumption of a periodic trajectory. This allows the da Vinci patient-side manipulators to track an actual heart trajectory with sub-millimetre error. Further, to simulate a virtually stabilized environment, we present the novel concept of maintaining the camera fixed relative to the heart target, effectively decoupling the vision tracking and arm tracking challenges. Finally, we executed preliminary experiments to evaluate surgeons' ability to perform simulated suturing and peg transfer tasks on a moving target. Performance for the simulated suturing was evaluated based on task completion time, accuracy of needle placement, and number of errors. For the suture task, the number of missed targets decreased from 37% to 13% when compensation was enabled, the number of hit targets increased from 26% to 41%, and completion time decreased. For the peg transfer tasks, again completion time and number of errors were measured. Though the margin for error was larger, there was less perceived difficulty of the task when compensation was enabled.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".