A computer-assisted protocol for endovascular target interventions using a clinical MRI system for controlling untethered microdevices and future nanorobots
Bibliographic record
Abstract
The possibility of automatically navigating untethered microdevices or future nanorobots to conduct target endovascular interventions has been demonstrated by our group with the computer-controlled displacement of a magnetic sphere along a pre-planned path inside the carotid artery of a living swine. However, although the feasibility of propelling, tracking and performing real-time closed-loop control of an untethered ferromagnetic object inside a living animal model with a relatively close similarity to human anatomical conditions has been validated using a standard clinical Magnetic Resonance Imaging (MRI) system, little information has been published so far concerning the medical and technical protocol used. In fact, such a protocol developed within technological and physiological constraints was a key element in the success of the experiment. More precisely, special software modules were developed within the MRI software environment to offer an effective tool for experimenters interested in conducting such novel interventions. These additional software modules were also designed to assist an interventional radiologist in all critical real-time aspects that are executed at a speed beyond human capability, and include tracking, propulsion, event timing and closed-loop position control. These real-time tasks were necessary to avoid a loss of navigation control that could result in serious injury to the patient. Here, additional simulation and experimental results for microdevices designed to be targeted more towards the microvasculature have also been considered in the identification, validation and description of a specific sequence of events defining a new computer-assisted interventional protocol that provides the framework for future target interventions conducted in humans.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 0.002 |
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".