Remote controlled robot assisted cardiac navigation: feasibility assessment and validation in a porcine model
Bibliographic record
Abstract
BACKGROUND: Despite the recent advances in catheter design and technology, intra-cardiac navigation during electrophysiology procedures remains challenging. Incorporation of imaging along with magnetic or robotic guidance may improve navigation accuracy and procedural safety. In the present study, the in vivo performance of a novel remote controlled Robot Assisted Cardiac Navigation System (RACN) was evaluated in a porcine model. METHODS: The navigation catheter and target sensor were advanced to the right atrium using fluoroscopic and intra-cardiac echo guidance. The target sensor was positioned at three target locations in the right atrium (RA) and the navigation task was completed by an experienced physician using both manual and RACN guidance. The navigation time, final distance between the catheter tip and target sensor, and variability in final catheter tip position were determined and compared for manual and RACN guided navigation. RESULTS: The experiments were completed in three animals and five measurements recorded for each target location. The mean distance (mm) between catheter tip and target sensor at the end of the navigation task was significantly less using RACN guidance compared with manual navigation (5.02 ± 0.31 vs. 9.66 ± 2.88, p = 0.050 for high RA, 9.19 ± 1.13 vs. 13.0 ± 1.00, p = 0.011 for low RA and 6.77 ± 0.59 vs. 15.66 ± 2.51, p = 0.003 for tricuspid valve annulus). The average time (s) needed to complete the navigation task was significantly longer by RACN guided navigation compared with manual navigation (43.31 ± 18.19 vs. 13.54 ± 1.36, p = 0.047 for high RA, 43.71 ± 11.93 vs. 22.71 ± 3.79, p = 0.043 for low RA and 37.84 ± 3.71 vs. 16.13 ± 4.92, p = 0.003 for tricuspid valve annulus. RACN guided navigation resulted in greater consistency in performance compared with manual navigation as evidenced by lower variability in final distance measurements (0.41 vs. 0.99 mm, p = 0.04). CONCLUSION: This study demonstrated the safety and feasibility of the RACN system for cardiac navigation. The results demonstrated that RACN performed comparably with manual navigation, with improved precision and consistency for targets located in and near the right atrial chamber.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".