Auto‐Threshold quantification of late gadolinium enhancement in patients with acute heart disease
Bibliographic record
Abstract
PURPOSE: To assess the Otsu-Auto-Threshold (OAT) for accuracy and reproducibility for sizing irreversible injury in late gadolinium enhancement (LGE) images of patients with acute heart disease. The OAT method automatically identifies high signal intensity areas using a cutoff derived from the signal intensity histogram and therefore is user-independent. MATERIALS AND METHODS: LGE was performed in 28 patients with acute myocardial infarction (MI) and 30 patients with acute myocarditis. LGE mass was compared between OAT and thresholds using 2 standard deviations (SD), 3SD, and 5SD above remote myocardium, and full-width-at-half-maximum (FWHM). A separate, blinded visual assessment served as the standard of truth. RESULTS: In patients with acute MI, OAT and 5SD did not differ (26.1 ± 11.4 g vs. 25.4 ± 11.1 g, P = 0.088), but thresholds of 2SD and 3SD overestimated LGE mass by 37% and 20%, respectively, and FWHM underestimated by 15%. In acute myocarditis, OAT was not different from a visual quantification, but thresholds of 2SD and 3SD overestimated LGE mass by 46% and 19%, respectively, and thresholds of 5SD and FWHM underestimated LGE mass by 17% and 26%, respectively. OAT and FWHM showed the best intraobserver and interobserver reproducibility. CONCLUSION: Automatic thresholding using OAT may serve as an accurate and reproducible method to quantify irreversible myocardial injury in acute heart disease.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".