Cardiovascular imaging research and innovation in 2023
Bibliographic record
Abstract
In 2023, cardiovascular imaging has made significant advancements, in terms of technology, pathophysiology, and clinical application. In this review, the most recent research findings in the field of cardiovascular imaging are discussed. Artificial intelligence and large population cohorts, together with several technical improvements, have had a crucial impact on the technological advancements of echocardiography, cardiovascular magnetic resonance, computed tomography (CT), and nuclear medicine. In the field of ischaemic heart disease, it has been demonstrated that appropriate non-invasive imaging strategies improve patients' management and reduce invasive procedures and the need for additional testing at follow-up. Moreover, improvements in plaque characterization with CT are an expanding field of research with relevant implications for the prediction of disease severity, evolution, and response to treatment. In the field of valvular heart disease, imaging techniques have advanced alongside improvements in transcatheter treatment for aortic stenosis, mitral, and tricuspid regurgitation. Finally, in the field of heart failure and cardiomyopathies, cardiovascular imaging has reinforced its crucial role in early diagnosis and risk evaluation, showcasing advanced techniques that outperform traditional methods in predicting adverse outcomes.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".