Deep learning-based epicardial adipose tissue measurement, maximizing prognostic information from attenuation correction imaging
Bibliographic record
Abstract
Epicardial Adipose Tissue (EAT) volume has been associated with risk of cardiovascular events, but manual annotation is time-consuming and only performed on gated Computed Tomography (CT). We developed a Deep Learning (DL) model to segment EAT from gated and ungated CT, then evaluated the association between EAT volume and death or Myocardial Infarction (MI). We included 7712 patients from three sites, two with ungated CT and one using gated CT. Of those, 500 patients from one site with ungated CT were used for model training and validation and 3,701 patients from the remaining two sites were used for external testing. Threshold for abnormal EAT volume (⪆144mL) was derived in the internal population based on Youden’s index. DL EAT measurements were obtained in ⪅2 seconds compared to approximately 15 minutes for expert annotations. Excellent Spearman correlation between DL and expert reader on an external subset of N=100 gated (0.94, p⪅0.001) and N=100 ungated (0.91, p⪅0.001). During median follow-up of 3.1 years (IQR 2.1 – 4.0), 306(8.3%) patients experienced death or MI in the external testing populations. Elevated EAT volume was associated with an increased risk of death or MI for gated (hazard ratio [HR] 1.72, 95% CI 1.11-2.67) and ungated CT (HR 1.57, 95% CI 1.20 – 2.07), with no significant difference in risk (interaction p-value 0.692). EAT volume measurements provide similar risk stratification from gated and ungated CT. These measurements could be obtained on chest CT performed for a large variety of indications, potentially improving risk stratification.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".