Ex Vivo Validation of Magnetically Actuated Intravascular Untethered Robots in a Clinical Setting
Bibliographic record
Abstract
Abstract Intravascular surgical instruments necessitate precise navigation within narrow vessels, requiring maximum flexibility, minimal diameter, and high degrees of freedom. However, existing tools often lack adequate control during insertion due to undesirable bending, which not only limits vessel accessibility but also poses a risk of tissue damage. The advancement of nextgeneration instruments aims to develop hemocompatible untethered devices that achieve precise control through external wireless forces and torques applied from outside the body. However, achieving this goal remains complex due to the challenges involved in testing and implementing untethered magnetic robots (UMRs) in clinical environments. These challenges encompass wireless actuation, non-invasive localization, biocompatibility, control robustness, and effective engagement. In this study, we assess the operational effectiveness of hemocompatible UMRs in clinical settings. We utilize an ex vivo porcine aorta model that accounts for factors such as varying blood-vessel diameters, varying blood flow rates, and the presence of renal aortic side-branches. This comprehensive approach enables us to analyze the robots’ motion dynamics within vessels, closely simulating real physiological and anatomical conditions. Our findings, in the absence of clotting, reveal a consistent linear reduction in UMR swimming speed against increasing arterial blood flow. Furthermore, our experiments showcase the UMR’s ability to generate sufficient force to navigate against a maximum arterial flow rate of 67 mL/min. Using the predicted and observed physical interactions of the UMRs, we effectively demonstrate preliminary proof-of-concept locomotion in a difficult-to-access target site, allowing us to navigate through the abdominal aorta and successfully reach the distal end of the renal artery.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".