Robotic Interventions: Achievements, Challenges, and Future Prospects
Bibliographic record
Abstract
to replace manual operations of continuum systems such as catheters and endoscopes. Many of such operations are lengthy and often depend on fluoroscopy (X-ray) for guiding the device. The occupational hazards in medical interventions are serious. The advantages of robotic operations include releasing the interventionists from exposure to hazardous radiation, improving ergonomic factors, integrating the precision of robots into operations, less dependency on the operator's skills, and possibility for multi-tasking. The primary focus of this talk will be on robotic cardiovascular catheterization in which two or more catheters are operated from a distance. Despite promising aspects of robotic catheterization, many modeling, sensing and control issues remain to be addressed. In addition to the characteristic issues of catheters (such as severe nonlinearities, coupled mechanics, under-actuation, low stiffness and dexterity), their operation in confined spaces also imposes major constraints on sensing and servo feedback. This presentation will provide an overview of recent advances on robotic cardiac catheterization. First, non-conventional modeling approaches for catheters will be reviewed. Next, novel sensing and estimation techniques, and servo control structures for semi-autonomous catheterization will be presented. Finally future directions of research will be outlined. The results of this research can potentially be used to enable manipulating soft longitudinal structures in different scales, opening the door to new frontier in many disciplines such as biology, medicine, material science, and manufacturing.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".