Preclinical Evaluation of the Stealth Autoguide Robotic Guidance Device for Stereotactic Cranial Surgery: A Human Cadaveric Study
Bibliographic record
Abstract
INTRODUCTION: Stereotactic procedures are routinely performed for brain biopsies, deep brain stimulation, and placement of stereoelectroencephalography (SEEG) electrodes for epilepsy. The recently developed Stealth Autoguide (Medtronic, Minneapolis, MN, USA) device does not require patients to don a stereotactic frame. In this preclinical study, we sought to quantitatively compare the Stealth Autoguide robotic system to 2 devices commonly used in clinical practice: the Navigus biopsy system (Medtronic) and the Leksell stereotactic frame (Elekta Ltd., Stockholm, Sweden). METHODS: In the first experimental setup, we compared target accuracy of the Stealth Autoguide to the Navigus system by using phantom heads filled with gelatin to simulate the brain tissue. In the second experimental setup, we inserted SEEG electrodes to targets within cadaveric heads in a simulated operating room environment. RESULTS: Using a homogeneous gelatin-filled phantom 3D reconstruction of a human head, we found that using the Stealth Autoguide system, while maintaining accuracy, was faster to use than the Navigus system. In our simulated operating room environment using nonliving human cadaveric heads, we found the accuracy of the Stealth Autoguide robotic device to be comparable to that of the Leksell frame. DISCUSSION/CONCLUSION: These results compare the use of the Stealth Autoguide robotic guidance system with commonly used stereotactic devices, and this is the first study to compare its use and accuracy with the Leksell frame. These findings provide mounting evidence that Stealth Autoguide will have potential clinical uses in various stereotactic neurosurgical procedures.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".