Theoretical and experimental quantification of carotid plaque volume measurements made by three‐dimensional ultrasound using test phantoms
Bibliographic record
Abstract
An accurate technique that exhibits low variability has practical importance for the quantification of carotid plaque volume. Such a technique is necessary to monitor plaque progression or regression that may result in response to nonsurgical therapy. In this study, we investigate the accuracy and variability of plaque volume measurement by three-dimensional ultrasound using vascular plaque phantoms over a range of 68.2 mm3 to 285.5 mm3. The agar plaques maintained a consistent cylindrical geometry with variations in the height, length, and echogenicity. The volume of each plaque was determined by water displacement. The three-dimensional (3D) ultrasound (US) images were acquired with a mechanical scanning system which creates a 3D US Cartesian volume, that was manipulated and viewed in any orientation, from a collection of conventional parallel two-dimensional (2D) US images. The plaque volumes were measured by serial 2D manual planimtery. The mean accuracy in plaque volume measurement was 3.1+/-0.9%. Variability in plaque volume measurement was calculated to be 4.0+/-1.0% and 5.1+/-1.4% for intraobserver and interobserver measurements, respectively. We have also developed a theoretical description for the variance in measurement of plaque volume using manual planimetry. Root-mean-square difference between experimentally and theoretically determined values of plaque volume fractional variance was 9%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".