On the influence of external force induced by the ultrasound probe on internal carotid artery elastography features
Bibliographic record
Abstract
Carotid strain imaging aims to quantify the deformation and translation (motion) of the artery wall and plaque. Due to changes in boundary conditions, external manual forces may induce a bias in the estimation of carotid artery mechanical properties. The purpose of this study was to investigate the impact of the compression force induced by the ultrasound probe during scanning on measured mechanical properties. Nine volunteers underwent an elastography exam of their left internal carotid artery (LICA) using a custom made force feedback handles that allowed the sonographer to control the pressure applied by the probe on the skin. The force was first recorded during normal scanning, as per operator standard practice (SP). Then, predefined compression (PDC) forces were adjusted with sound feedback to randomly selected values between 2, 4, 6, 8, 10, and 12 Newton. Radiofrequency images were acquired and converted to B-mode for automated wall segmentation. Six carotid elastography features were assessed on upper and lower walls. Results indicated that the sonographer under SP scanned with a force of 6.4 ± 1.6 Newton. Statistically significant negative linear regressions were observed between the applied force and every elastography features on the upper wall, and with only the cumulated lateral translation on the lower wall. One way ANOVA showed that some features were significantly different between PDC versus SP. In summary, slightly negative linear influence of the applied pressure on elastography features was observed. The variation of all elastography features as a function of the probe pressure mainly affected upper wall measurements.
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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 | 0.010 |
| 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) | 0.002 | 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".