Toward Robot-Assisted Photoacoustic Imaging: Implementation Using the da Vinci Research Kit and Virtual Fixtures
Bibliographic record
Abstract
Photoacoustic imaging of the prostate is challenging due to the limited access and limited acoustic windows to the prostate gland. We aim to develop intraoperative prostate photoacoustic imaging using the da Vinci robotic system and a pick-up ultrasound transducer that can be easily picked up and manipulated by the robot. We propose a new approach in which the da Vinci robot is programmed to acquire trajectories in a shared control configuration with “virtual fixtures”; the pick-up transducer is controlled so that it stays parallel to a single axis defined as the tomography axis, and its translation is fixed to a single plane normal to this axis. The surgeon controls the transducer motion on the tissue along this virtual fixture while the laser is fired and photoacoustic data are collected periodically. The RMS errors of the photoacoustic tomography images are 0.06 a.u. This study confirms that intraoperative da Vinci robot-assisted photoacoustic imaging with a pick-up transducer is feasible.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".