Automatic Detection and Quantification of Carotid Atherosclerotic Plaque Parameters from B-mode Ultrasound Images Using Deep Learning
Bibliographic record
Abstract
Carotid atherosclerosis is a major cause of cerebrovascular events, including ischemic strokes. B-mode ultrasound (US) is a safe, widely available, and cost-effective imaging modality used to detect and assess atherosclerotic plaques. However, it has limitations, namely, high inter-operator and skill-dependent variability in interpretation. We developed a time-efficient deep convolutional neural network (CNN)-based workflow to automatically 1) detect and segment plaques from US (longitudinal/transverse), and 2) quantify plaque parameters: thickness (T), total plaque area (TPA), and total plaque volume (TPV). Patients (n=141) with severe carotid atherosclerotic plaques underwent US examination prior to carotid endarterectomy. A radiologist annotated US images (467), longitudinal (326) and transverse (141). The Intersection Over Union between manual and algorithm-based segmentation was 0.80±0.18 (mean±standard deviation) on 46 unseen US images. The mean absolute error of (∆T), (∆TPA), and (∆ V) were 0.22 mm, 0.73 mm2, and 0.89 mm3. The total time (detection/quantifications) was <1 second per image.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".