Deep learning-based quantification of epicardial adipose tissue volume from non-contrast computed tomography images: a multi-centre study
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Bibliographic record
Abstract
Abstract Aims Epicardial adipose tissue (EAT), located within the pericardial sac, has emerged as a biomarker for coronary artery disease (CAD) progression. This study aimed to develop and validate a deep learning-based system for automated EAT volume quantification using non-contrast computed tomography (NCCT) scans from a large, multi-centre, pan-Asian cohort. Methods and results A total of 1243 NCCT patient scans from three centres were used to train and internally validate a deep learning model based on 3D UNet++ architecture for pericardium segmentation, followed by intensity thresholding to derive EAT volume. Epicardial adipose tissue quantification required ∼30 s per scan. The final model was evaluated on an external testing cohort of 160 patients, including 90 non-Asian individuals. In this cohort, AI-predicted EAT volumes showed excellent agreement with expert annotations (r = 0.975; P < 0.0001). The Bland–Altman analysis demonstrated a mean bias of −5.2 cm3with 95% limits of agreement from −25.1 to 14.7 cm3. Among the non-Asian subgroup, model performance remained strong (r = 0.970; bias, −3.2 cm3; limits of agreement, −25.1–18.7 cm3). AI-derived EAT volume was independently associated with obstructive CAD (odds ratio 1.11; 95% confidence interval, 1.04–1.19; P = 0.004), after adjusting for confounders. The global χ2 statistic increased from 81.7 with coronary calcium score alone to 93.3 when EAT volume was added (P = 0.001), indicating improved risk prediction. Conclusion We developed and validated a deep learning system for automated EAT volume quantification from NCCT scans. The model demonstrated high accuracy and generalizability across ethnically diverse populations, supporting its potential for routine EAT assessment and CAD risk stratification. Trial Registration ClinicalTrials.gov Identifier: NCT05509010.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it