Comparison of Particle Swarm Optimization and Genetic Algorithm for Ultrasound Estimation of Carotid Intima-Media Thickness Using Matching Pursuit
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) are one of the most dominant causes of death worldwide. Consequently, much attention has been given to CVD prevention by monitoring CVD risk factors. The intima-media thickness (IMT) of the carotid artery is widely recognized as a reliable indicator for CDV risk and can be monitored non-invasively using ultrasound. To reduce IMT estimation variability, many automatic algorithms that estimate IMT using B-mode ultrasound images have been proposed. The performance of B-mode image-based algorithms are often limited by the image resolution. M-mode or A-mode algorithms may be an alternative to B-mode approaches for lower resolution ultrasound data. Recently, matching pursuit (MP) signal decomposition with particle swarm optimization (PSO) was investigated for extracting the arterial tissue interface echoes used for IMT estimation using A-mode ultrasound signals. In this work, PSO was compared to the genetic algorithm (GA) for IMT estimation using MP. Both methods were evaluated on the frequency at which the arterial tissue interface echoes used for IMT estimation were successfully extracted, the time and number of computations required to decompose the ultrasound signals, and the variability in IMT estimates. Using the same search constraints and control parameters for both methods, the results showed that the MP algorithm with PSO was faster and required less computations than the one with the GA, but the GA resulted in more successful IMT estimates than PSO. No significant difference in IMT estimation variability was observed.
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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 | 0.002 | 0.004 |
| 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".