Diagnosis of Vulnerable Atherosclerotic Plaques by Time-Resolved Fluorescence Spectroscopy and Ultrasound Imaging
Bibliographic record
Abstract
In this study, time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) and ultrasonography were applied to detect vulnerable (high-risk) atherosclerotic plaque. A total of 813 TR-LIFS measurements were taken from carotid plaques of 65 patients, and subsequently analyzed using the Laguerre deconvolution technique. The investigated spots were classified by histopathology as thin, fibrotic, calcified, low-inflamed, inflamed and necrotic lesions. Spectral and time-resolved parameters (normalized intensity values and Laguerre expansion coefficients) were extracted from the TR-LIFS data. Feature selection for classification was performed by either analysis of variance (ANOVA) or principal component analysis (PCA). A stepwise linear discriminant analysis algorithm was developed for detecting inflamed and necrotic lesion, representing the most vulnerable plaques. These vulnerable plaques were detected with high sensitivity (>80%) and specificity (>90%). Ultrasound (US) imaging was obtained in 4 carotid plaques in addition to TR-LIFS examination. Preliminary results indicate that US provides important structural information of the plaques that could be combined with the compositional information obtained by TR-LIFS, to obtain a more accurate diagnosis of vulnerable atherosclerotic plaque.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".