Abstract 11609: Developing a Novel Method for Measuring Relative Lung Perfusion at the Catheterization Laboratory
Bibliographic record
Abstract
Background: Maldistribution of pulmonary blood flow (Qp) is common in patients with congenital heart disease and is associated with worse clinical outcomes. Currently, measurement of Qp-split can only be performed outside the catheterization laboratory, using either lung perfusion scans or cardiac magnetic resonance imaging. Aim: We sought to develop and evaluate a tool for measuring Qp-split using readily available fluoroscopy sequences. Methods: A retrospective cohort study of patients with conotruncal anomalies who underwent perfusion scan and subsequent cardiac catheterization. Inclusion criteria were non-selective angiogram of pulmonary vasculature, oblique angulation ≤20°, and an adequate view of both lung fields. A method was developed and implemented in 3D Slicer to calculate the amount of contrast that entered each lung field from the start of contrast-injection and until the onset of levophase. The predicted perfusion distribution was compared to the distribution measured by lung scan. Results: In total 32% (79/249) of screened studies met inclusion criteria. There was strong correlation between the predicted Qp-split and the measured Qp-split (R 2 =0.83, p<0.001) with median absolute error (MAE) of 6%. Bias was not systematically worse at either extreme of flow distribution. Factors associated with better prediction were smaller BSA, younger age, right ventricle (vs. pulmonary artery) angiograms, and cranial angulations ≤20°. In cases with one or more of these conditions (n=40), the prediction achieved R 2 =0.87, MAE of 5.5%, and 78% of predictions were within 10% of true flow. Conclusions: Data from conventional angiograms can be used to provide accurate real-time measurement of relative perfusion of the left and right lungs. This has the potential to reduce unnecessary testing, associated costs, and radiation exposure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".