Detection of inflating balloon in optical coherence tomography images of a porcine artery in a beating heart experiment
Bibliographic record
Abstract
Suboptimal results of angioplasty procedures have been correlated to arterial damage during balloon inflation. We propose to monitor balloon inflation during the angioplasty procedure by detecting the balloon contours with intravascular optical coherence tomography (IVOCT). This will shed more light on the interaction between the balloon and the artery and to assess the artery's mechanical response. An automatic edge detection algorithm is applied for detection of the outer surface of an inflating balloon in a porcine artery in a beating heart experiment. A compliant balloon is inflated to deform the artery. IVOCT monitoring of balloon inflation is performed at a rate of 30 frames per second. During inflation, the balloon engages the arterial wall. Therefore, the characterization of the diameter of the inflated balloon leads to a characterization of the luminal diameter of the vessel. This provides precise information about the artery response to a simulated angioplasty procedure, information currently not provided by any other existing technique. In the current experiment, balloon inflation characterization is based on 356 IVOCT frames during which the estimated balloon diameter increases approximately from 1.8 mm to 2.9 mm.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".