Real-time Control of Angioplasty Balloon Inflation Based on Feedback from Intravascular Optical Coherence Tomography: Experimental Validation on an Excised Heart and a Beating Heart Model
Bibliographic record
Abstract
We report on real-time control of balloon inflation inside porcine arteries. In the first step, experiments were done in a coronary artery of an excised heart. In the second step, experiments were done in a beating heart setup providing conditions very close to in vivo conditions without the complications. A programmable syringe pump was used to inflate a compliant balloon in arteries, while intravascular optical coherence tomography (IVOCT) monitoring was performed. In a feedback loop, IVOCT images were processed to provide the balloon diameter values in real time to control the pump action in order to achieve a target diameter. In different experiments, various flow rates and target diameters were used. In the excised heart experiment, there was good convergence to target diameters resulting in a satisfactory balloon inflation control. In the beating heart experiment, there were oscillations in the diameter values due to cyclic arterial contractions. In these experiments, the control system maintained diameter averages satisfactorily close to predetermined target values. Real-time control of balloon inflation could not only provide a safer outcome for angioplasty procedures, but could also provide additional information for diagnostics since it implicitly provides information about the artery response to the inflation process.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".