Opportunistic Identification of Coronary Artery Calcium and Valve/Vascular Calcifications on Chest X-Ray: Improvements With Single-ExposureDual-Energy Imaging
Bibliographic record
Abstract
Purpose: To evaluate whether single-exposure, dual-energy chest X-ray (DEX) improves visualization of coronary artery calcium (CAC) and valve/vascular calcifications compared to conventional X-ray. Materials and Methods: Sixty-one bone-marrow transplant patients (22- 79 years; median 61; IQR 15; w/m, 24/37), underwent single-exposure dual-energy X-ray (Reveal 35C, KA imaging) in pa and lateral projection, followed by a standard-of-care chest CT. Two DEX pairs (pa/lateral) were calculated: a composite image (COMP) and a bone image with soft-tissue subtraction (BI). The COMP pair was reviewed by 2 chest radiologists, assessing the presence/absence of CAC and valve/vascular calcifications on a confidence scale from −2 (confidently not present) to 2 (confidently present). Subsequently, the BI pair was revealed, and readers reevaluated both pairs (COMP and BI) jointly using the identical scale. CTCAC scores were categorized according to the CAC-DRS (0-3) and served as standard of reference, valve/vascular calcifications were categorized on CT as present or absent. Results: For detecting CAC on DEX in any CAC-DRS category (1-3), in category 2-3, in category 3, and for valve/vascular calcifications, the ROC-AUC (combined for both readers) for COMP images was 0.74 (CI: 0.64-0.84), 0.81 (CI: 0.68-0.94), 0.84 (CI: 0.69-0.98), and 0.90 (CI: 0.83-0.99), and for the BI images 0.91 (CI: 0.83-0.98), 0.94 (CI: 0.86- 1.00), 0.89 (CI: 0.77-1.00), and 0.98 (CI: 0.96-1.00), with P = .0003, P = .044, P = .42, and P = .55, respectively. The Intraclass-Correlation-Coefficient (ICC) for CAC on COMP/BI was 0.973/0.954, and for valve/vascular calcifications 0.971/0.965. Conclusion: Single-exposure, dual-energy acquisition improves diagnostic confidence for coronary artery calcium and valve/vascular calcification identification on chest X-rays.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".