Peripheral Interventions Radiation Exposure Reduction Using a Sensor-Based Navigation System: A Proof-of-Concept Study
Bibliographic record
Abstract
Background Intravascular catheter positioning is done with radiography imaging. Increasing evidence indicates excessive ionizing radiation exposure for patients and physicians during catheterization procedures, making solutions to reduce radiation exposure a priority. This study evaluated the feasibility and impact of using sensor-based magnetic navigation on (i) fluoroscopy time and (ii) positioning accuracy and safety of a peripheral angioplasty balloon catheter. Methods All patients (n = 10) underwent a balloon-positioning protocol using 2 navigation methods sequentially: (i) magnetic navigation with minimal fluoroscopy; (ii) fluoroscopic navigation. The navigation method order was randomized, and 4 consecutive placements per method were performed. A target vascular bifurcation was used as a fiduciary landmark for both methods to determine accuracy. Results Balloon placements were successful with both navigation methods in all subjects, and no adverse events occurred. Magnetic guidance led to significant reductions in fluoroscopy time (0.37 ± 1.5 vs 15.0 ± 8.1 seconds, P < 0.001) and dose (0.3 ± 1.2 vs 24.1 ± 23.8 μGy.m 2 , P < 0.01). The time duration for balloon alignment was similar for the 2 navigation methods (4.8 ± 1.4 vs 4.8 ± 2.3 seconds, P = 0.89), and the accuracy was almost identical (0.51 ± 0.41 vs 0.51 ± 0.32 mm, P = 0.97). Conclusions These results demonstrate the feasibility of using sensor-based magnetic guidance during simple peripheral interventional procedures; a significant reduction in ionizing radiation was achieved, with excellent positioning accuracy and safety. The clinical applications of magnetic guidance for device navigation during more complex percutaneous procedures should be evaluated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".