Cardiovascular CT and MRI in 2020: Review of Key Articles
Bibliographic record
Abstract
Despite the global coronavirus pandemic, cardiovascular imaging continued to evolve throughout 2020. It was an important year for cardiac CT and MRI, with increasing prominence in cardiovascular research, use in clinical decision making, and in guidelines. This review summarizes key publications in 2020 relevant to current and future clinical practice. In cardiac CT, these have again predominated in assessment of patients with chest pain and structural heart diseases, although more refined CT techniques, such as quantitative plaque analysis and CT perfusion, are also maturing. In cardiac MRI, the major developments have been in patients with cardiomyopathy and myocarditis, although coronary artery disease applications remain well represented. Deep learning applications in cardiovascular imaging have continued to advance in both CT and MRI, and these are now closer than ever to routine clinical adoption. Perhaps most important has been the rapid deployment of MRI in enhancing understanding of the impact of COVID-19 infection on the heart. Although this review focuses primarily on articles published in Radiology, attention is paid to other leading journals where published CT and MRI studies will have the most clinical and scientific value to the practicing cardiovascular imaging specialist. © RSNA, 2021
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".