Development of a Robotic System to Enable Beating-heart Surgery
Bibliographic record
Abstract
Performing a surgical procedure on a beating heart is nearly impossible as the surgeon must simultaneously follow the heart’s motion and perform a surgical task. Assume the position of a target point for operation on the heart’s (interior or exterior) surface is captured in ultrasound images. If a robotic system could move a surgical tool in synchrony with this target point while the heart beats, the surgeon could then perform the surgical procedure as if the beating heart were stationary. This paper discusses the electromechanical and control design issues in such a system. An experimental testbed is described that consists of an optical motion tracker to simulate the function of an ultrasound imager, a mechanical heart motion simulator, and a voice-coil actuator for holding the surgical tool. An approach based on a Smith predictor is proposed to compensate for the delay introduced by the required image acquisition and processing. Another issue is the slowly (20 Hz) sampled data from the ultrasound images, which is upsampled to 100 Hz using either a zero-order-hold or a cubic interpolator. Experimental results are reported with the goal of having the surgical tool follow the combined motion of the surgeon and the beating heart.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".