Smith Predictor-Based Robot Control for Ultrasound-Guided Teleoperated Beating-Heart Surgery
Bibliographic record
Abstract
Performing surgery on fast-moving heart structures while the heart is freely beating is next to impossible. Nevertheless, the ability to do this would greatly benefit patients. By controlling a teleoperated robot to continuously follow the heart's motion, the heart can be made to appear stationary. The surgeon will then be able to operate on a seemingly stationary heart when in reality it is freely beating. The heart's motion is measured from ultrasound images and thus involves a non-negligible delay due to image acquisition and processing, estimated to be 150 ms that, if not compensated for, can cause the teleoperated robot's end-effector (i.e., the surgical tool) to collide with and puncture the heart. This research proposes the use of a Smith predictor to compensate for this time delay in calculating the reference position for the teleoperated robot. The results suggest that heart motion tracking is improved as the introduction of the Smith predictor significantly decreases the mean absolute error, which is the error in making the distance between the robot's end-effector and the heart follow the surgeon's motion, and the mean integrated square error.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".