Manual planimetric measurement of carotid plaque volume using three‐dimensional ultrasound imaging
Bibliographic record
Abstract
We investigated the utility of three manual planimetric methods to quantify carotid plaque volume. A single observer measured 15 individual plaques from 15 three-dimensional (3D) ultrasound (3D US) images of patients ten times each using three different planimetric approaches. Individual plaque volumes were measured (range: 32.6-597.1 mm3) using a standard planimetric approach (M1) whereby a plaque end was identified and sequential contours were measured. The same plaques were measured using a second approach (M2), whereby plaque ends were first identified and the 3D US image of the plaque was then subdivided into equal intervals. A third method (M3) was used to measure total plaque burden (range: 165.1-1080.0 mm3) in a region (+/- 1.5 cm) relative to the carotid bifurcation. M1 systematically underestimated individual plaque volume compared to M2 (V2 = V1 + 14.0 mm3, r = 0.99, p = 0.006) due to a difference in the mean plaque length measured. Coefficients of variance (CV) for M1 and M2 decrease with increasing plaque volume, with M2 results less than M1. Root mean square difference between experimental and theoretical CV for M2 was 3.2%. The standard deviation in the identification of the transverse location of the carotid bifurcation was 0.56 mm. CVs for plaque burden measured using M3 ranged from 1.2% to 7.6% and were less than CVs determined for individual plaque volumes of the same volume. The utility of M3 was demonstrated by measuring carotid plaque burden and volume change over a period of 3 months in three patients. In conclusion, M2 was determined to be a more superior measurement technique than M1 to measure individual plaque volume. Furthermore, we demonstrated the utility of M3 to quantify regional plaque burden and to quantify change in plaque volume.
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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".