Validation of a three-dimensional intravascular ultrasound imaging technique to assess atherosclerotic burden: potential for improved assessment of cardiac allograft coronary artery disease.
Bibliographic record
Abstract
BACKGROUND: Serial analysis of intracoronary ultrasound images is limited by difficulty with spatial registration and inability to assess the full extent of vascular disease. Three-dimensional (3D) imaging of coronary arteries can potentially overcome these limitations. OBJECTIVES: To assess the feasibility of using a PC-based 3D rendering technique to assess atherosclerotic burden. METHODS: To define the accuracy of 3D intravascular ultrasound (IVUS) measurements in vitro, six porcine iliac arteries and nine human cadaveric iliac arteries were pressure fixed and imaged with a commercial IVUS system. 3D datasets of the arteries were constructed, and measurements were correlated with histomorphometry. In vivo studies of 53 arterial segments (19 right coronary, 26 anterior descending and eight circumflex) were scanned in 18 patients, one month to nine years post-transplantation and correlated to corresponding angiographic images for the presence of atherosclerosis. RESULTS: Porcine artery length and volume measurements by IVUS showed a high degree of correlation with histomorphometry measurements (r=0.99, P<0.0003 and r=0.99, P<0.0001, respectively). Human arterial length, total artery volume and lumen volume measurements were similarly correlated (r=0.99, P<0.0001, r=0.99, P<0.0001 and r=0.98, P<0.0001, respectively). For plaque volume, r=0.84, P<0.05. In vivo 3D IVUS scans demonstrated atherosclerotic lesions in nine of 18 patients, compared with five detected by angiography alone. CONCLUSIONS: 3D IVUS imaging allows rapid and accurate measurement of arterial length, volume and plaque dimensions in addition to lumenal area and can demonstrate the full extent of atherosclerotic pathology. Because of its superior reproducibility, this technique may be used to assess the progression of coronary artery disease and allow for more accurate evaluation of interventions aimed at preventing or retarding coronary artery disease.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 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".