Cardiovascular imaging in 2024: review of current research and innovations
Bibliographic record
Abstract
Cardiovascular imaging saw significant advancements in 2024, impacting technology, pathophysiology, and clinical applications. This review provides a comprehensive summary of the most impactful research in cardiovascular imaging published in 2024, highlighting technological advancements, as well as research on ischaemic heart disease, valvular heart disease, cardiomyopathies, and heart failure. It emphasizes the crucial role of artificial intelligence, large-scale studies, and technical improvements across echocardiography, cardiovascular magnetic resonance, computed tomography (CT), and nuclear medicine. In the context of ischaemic heart disease, non-invasive imaging strategies improve patient management and reduce invasive coronary angiograms and unnecessary follow-up testing. Computed tomography plaque characterization is a growing area of research, with potential for predicting disease severity, atherosclerosis progression, and clinical outcomes. In valvular heart disease, several imaging studies focused not only on transcatheter treatments for aortic stenosis, mitral regurgitation, and tricuspid regurgitation but also on specific conditions such as mitral valve prolapse and mitral annular disjunction. Finally, for heart failure and cardiomyopathies, imaging plays a vital role in early diagnosis and risk assessment, with newer techniques surpassing traditional methods in providing morpho-function characterization and in predicting long-term outcomes.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".