Quantification of Roughness of Calcific Deposits in Computed Tomography Scans of Human Coronary Arteries
Bibliographic record
Abstract
OBJECTIVES: The incidence of coronary artery disease has been shown to be greater in patients with calcific deposits than in those without. It has been suggested that the pattern of distribution of coronary calcific deposits within coronary arteries is of greater predictive value for acute coronary events than the overall quantity. Whether roughness of calcific deposits is a predictor of acute coronary events is not known. We derived and tested an algorithm, Voxel-Based Bosselation (VBB), for noninvasive quantification of roughness of calcific deposits in human coronary arteries imaged by computed tomography (CT). METHODS AND RESULTS: VBB was tested on 213 coronary calcific deposits from electron beam CT scans of 27 patients. This algorithm evaluates the 3-dimensional connectedness of surface voxels of each deposit: smooth masses have low VBB and rough masses high VBB. The algorithm was calibrated with artificially generated phantoms as well as background noise mimicking calcific deposits and surrounding heart tissue. The VBB algorithm is applicable to calcific deposits of all scales and gradations. The VBB values of the deposits in this study did not correlate with deposit size further supporting its validity as a measurement of roughness. The VBB index corresponded directly with visual reconstruction using Phong-shaded algorithms. CONCLUSIONS: The VBB index, derived here, is a noninvasive method of quantifying the roughness of calcific deposits in CT scan data which can now be used in future clinical studies to determine possible correlations with increased plaque vulnerability and major acute coronary events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".