Design and experimental evaluation of an automated catheter operating system
Bibliographic record
Abstract
Manual catheter-based interventions (CBIs) suffer from exposure of the interventionalists to X-ray, and dependence of their performance on the expertise and fatigue level of the interventionalists. Robot-assisted catheterization systems (RACS) have been introduced in recent years to improve the efficiency of CBIs; however, using them is still associated with some difficulties such as set-up dependency to a specific type of intervention instrument, not being portable, and offering limited options of operation modes. The objective of this research is to develop a new RACS to address these shortcomings. We propose Althea II as an improvement for our previously introduced RACS, Althea I. Althea II is designed for both research purposes and clinical applications including catheter-based cardiovascular interventions. Althea II benefits from a novel structural design leading to a significantly reduced weight and making the device inclusive for a broader range of intervention instruments. Also, a tip detection algorithm is developed and integrated into the graphical user interface (GUI) to enable image-based navigation, and accordingly, fully automatic navigation. Althea II has improved the outcome of catheter-based interventions by increased accuracy and precision of the intervention. The system can navigate the catheter tip to a designated target with an accuracy higher than 90% in both velocity and positioning mode. The device is associated with an upgraded GUI equipped with a strong tip detection algorithm with an accuracy of 0.05 mm. Moreover, Althea II gains from a quicker assembly time (20 minutes, which equals five times faster). The independency from specific catheters, several modes of function, an imaged-based feedback control, portability, and a remote function should allow operation even from beginners and reduce X-ray exposure. The preliminary research studies verified the accuracy and repeatability of Althea II, demonstrated the feasibility and applicability of using the set-up in multiple applications, and highlighted the improved set-up capabilities over the currently available RACS.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".