Analysis of Hemodynamic Markers in Atrial Fibrillation Using Advanced Imaging Techniques
Bibliographic record
Abstract
Atrial fibrillation (AF) is a prevalent heart arrhythmia, characterized by an irregularly irregular rhythm and the absence of identifiable P waves on ECG. Given the loss of effective atrial contraction, AF carries a risk of serious complications. If untreated, AF can promote thrombogenesis, leading to stroke, systemic embolism (e.g., limb or organ ischemia), and myocardial infarction. These serious complications highlight the importance of understanding AF and assessing stroke risk to guide optimal management of this chronic arrhythmia. Congruent with recent technological developments, advanced imaging has emerged as a modality to better understand AF. This review highlights advanced imaging techniques and their advantages, with a focus on 4D flow MRI, a novel modality that enables visualization of blood flow patterns in three dimensions and provides unique insights into cardiac hemodynamics. It also synthesizes the current literature on key hemodynamic markers identified by 4D flow MRI, including blood flow stasis, wall shear stress, and vorticity. Quantifying these markers has improved predictive accuracy of future stroke risk in AF patients, allowing clinicians to risk stratifying their patients and optimize management. Finally, the review discusses potential future markers that may further refine our understanding of AF and inform patient care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".