ASSESSMENT OF CAROTID INTRAPLAQUE HEMORRAGE BY PHOTOACUSTICS IMAGING (PAI) IN PATIENTS UNDERGOING TROMBOENDOARTERIECTOMY: FIRST IN-VIVO HUMAN VALIDATION STUDY
Bibliographic record
Abstract
Objective: The aim of the study is the validation of a portable multimodal photoacoustic imaging (PAI) system, for the identification of intraplaque haemorrhage and compare with MRI and histology (gold standard). Design and method: 25 patients with carotid stenosis>70% and clinical indication to tromboendoarterectomy were recruited. Angio-MRI for intraplaque hemorrhage assessment (Cube sequence) was performed. PAI clips (5 seconds, Frame rate 1000/sec) were acquired. Each clip was scored for the presence of PAI signal by means of an integrated scoring system (semiquantitative, from 0 to 12). Semiquantitative grading scales were used to assess plaque histological features of hemorrage and vulnerability. Results: 18 patients had no missing MRI, PAI and histology data and were included in this analysis. Mean age was 73 ± 8 years, 60% men, 80% Caucasians, 92% hypertensives, 60% with a previous stroke. Only 3 plaques out of 21 showed no signs of intraplaque hemorrhage, 4 showed small hemorrage, while 14 (67%) showed large hemorrhages. PAI score (best cut-off >4) correctly classified 14 out of 18 patients (Sensitivity 73.3%, specificity 100%, AUC 0.867). MRI performance was substantially similar (Sensitivity 60%, specificity 100%, AUC 0.800, 12 patients correctly classified), with a non-significant difference in AUC compared to PAI (p = 0.420). Conclusions: In this first in-vivo human study, PAI is able to identify histological intraplaque hemorrhage with an excellent specificity and acceptable sensitivity, equivalent to MRI. The very high specificity, with a low number of false positives, make PAI a good candidate for evaluation of plaques prior to surgery to i.e. reinforce the decision to perform surgery.
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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 | 0.003 |
| 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) | 0.001 | 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".