Robot-assisted MRI-guided prostatic interventions
Bibliographic record
Abstract
SUMMARY This paper reports on recent progress made toward the development of a new magnetic resonance imaging (MRI)-compatible robot-assisted surgical system for closed-bore image-guided prostatic interventions: thermal ablation, radioactive seed implants (brachytherapy), and biopsy. Each type of intervention will be performed with a different image-guided, robot-based surgical tool mounted on the same MRI-guided robot through a modular trocar. The first stage of this development addresses only laser-based focal ablation. The robot mechanical structure, modular surgical trocar, control architecture, and current stage of performance evaluation in the MRI environment are presented. The robot actuators are ultrasonic motors. A methodology of using such motors in the MRI environment is presented. The robot prototype with surgical ablation tool is undergoing tests on phantoms in the MRI bore. The tests cover MRI compatibility, image visualization, robot accuracy, and thermal mapping. To date, (i) the images are artifact- and noise-free for certain scanning pulse sequences; (ii) the robot tip positioning error is less than 1.2 mm even at positions closer than 0.3 m from the MRI isocenter; (iii) penetration toward the target is image-monitored in near-real time; and (iv) thermal ablation and temperature mapping are achieved using a laser delivered on an optical fiber and MRI, respectively.
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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 | 0.000 |
| 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) | 0.002 | 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".