Development and validation of MRI compatible pediatric surgical robot with modular tooling for bone biopsy
Bibliographic record
Abstract
In clinical practice, magnetic resonance imaging (MRI) is used to locate a lesion/tumor for bone biopsy in children. However, there is a lack of MR-compatible tools that can be used simultaneously during imaging and biopsy while maintaining surgical accuracy and safety. The Pediatric Surgery Robot (PSR) platform is a 5-DOF robot with a modular tool interface. For the case of bone biopsy, a Bone Biopsy Tooling (BBT) is attached. It is designed to fit within a Philips Achieva 3.0T MRI bore and carry a modified titanium bone biopsy needle. A surgical pre-planning and control interface has been developed for joint and Cartesian level control. The PSR-BBT has demonstrated 1.65 +/- 1.77 mm accuracy in Cartesian control in free space. The PSR-BBT can generate 12.46 +/- 0.32 N of axial force while drilling at a speed of 30 rpm, which is sufficient for cortical and cancellous bone phantoms. Under MRI testing (T1-FFE, T1-SE, T2-FFE and T2-TSE scans), the system demonstrated less than 33% signal-to-noise ratio variation while drilling and a 0.46% geometric distortion while powered on without significantly impacting MRI guidance in situ. These results show that the PSR-BBT can allow the user to simultaneously image and perform the biopsy and presents the PSR as a viable platform for MR-guided robotic surgery.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".