Accuracy of aortic annular measurements obtained from three-dimensional echocardiography, CT and MRI: human in vitro and in vivo studies
Bibliographic record
Abstract
OBJECTIVES: To determine the accuracy of calcium-containing rings measurements imaged by three-dimensional echocardiography (3DE), multi-slice CT (MSCT) and cardiac magnetic resonance (CMR) under ideal conditions against the true ring dimensions. To compare the accuracy of aortic annulus (AoA) measurements in ex vivo human hearts using 3DE, MSCT and CMR. To determine the accuracy of AoA measurements in an in vivo human model. DESIGN: 3DE, MSCT and CMR imaging were performed on 30 calcium-containing rings and 28 explanted human hearts. Additionally, 15 human subjects with clinical indication for MSCT underwent 3DE. Two experts in each modality measured the images. MAIN OUTCOME MEASURES: Bias and intraclass correlation coefficient for accuracy of imaging measurements when compared with actual ring dimensions. Bias, intraclass correlation coefficient and variability were obtained: (1) when comparing explanted human heart AoA measurements from the two remaining imaging modalities with the most accurate one as determined from the ring measurements and (2) in in vivo human AoA measurements. Analysis was repeated on explanted heart subgroups divided by aortic valve Agatston score. RESULTS: Against the known ring dimensions, CMR had the highest accuracy and the lowest variability. MSCT measurements had high accuracy but wider variability and 3DE had the lowest accuracy with the largest variability. When 3DE and MSCT were compared with CMR, 3DE underestimated and MSCT overestimated AoA dimensions, but inter-measurement variability of 3DE and MSCT were similar. When divided by Agatston score, both 3DE and MSCT measurements were larger and showed greater variability with increasing calcium burden. The in vivo study showed that the correlation between 3DE and MSCT measurements was high; however, 3DE measurements were smaller than those measured with MSCT. CONCLUSIONS: In the in vitro model, CMR measurements were the most accurate for assessing the actual dimensions suggesting that further investigations on its role in AoA measurement in TAVR are needed. However from the in vivo model, MSCT and 3DE are reasonable alternatives with the understanding that they can slightly overestimate and underestimate annular dimensions, respectively.
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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".