Reproducible Classification of Infarct Heterogeneity Using Fuzzy Clustering on Multicontrast Delayed Enhancement Magnetic Resonance Images
Bibliographic record
Abstract
Delayed enhancement MRI (DE-MRI) can be used to identify myocardial infarct (MI). Classification of MI into the infarct core and heterogeneous periphery (called the gray zone) on conventional inversion-recovery gradient echo (IR-GRE) DE-MRI images has been related to inducibility for ventricular tachycardia. However, this classification is sensitive to image noise, depends on the signal intensity characteristics in a remote region of myocardium, and requires manual contours of the endocardial border. Image analysis and fuzzy clustering techniques were developed to analyze images acquired using a multicontrast delayed enhancement (MCDE) sequence in order characterize the infarct zones. The MCDE analysis is automated and uses data fitting of signal intensities acquired at multiple inversion times. In a study of 15 patients with chronic MI, the gray zones derived from IR-GRE and MCDE images were comparable. The variability in the gray zone size associated with random noise and operator input was significantly reduced using the MCDE-based analysis compared to the IR-GRE-based analysis. In summary, the MCDE approach yields a more reproducible measure of the infarct core and gray zones on any given data set.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".