Automated and semi-automated 3D echocardiographic software for aortic annulus sizing in transcatheter aortic valve implantation helps bridge the gap between expert and novice operators
Bibliographic record
Abstract
3D-transesophageal echocardiography (3D-TEE) is an alternative to multidetector row computed tomography (MDCT) for aortic annulus (AoA) sizing in preparation for Transcatheter aortic valve implantation (TAVI). We aim to evaluate how the fully automated (auto) and semi-automated (SA) TEE methods perform compared to conventional manual TEE method and the gold standard MDCT for annulus sizing both in expert and novice operators. In this prospective cohort study, eighty-nine patients with severe aortic stenosis underwent multimodality imaging with 3D-TEE and MDCT. Annular measurements were collected by expert echocardiographers using 3D auto, SA and manual methods and compared to MDCT. A novice in the field of echocardiography retrospectively measured the AoA for all patients using the same methods. TEE measurements, independently of the method used, had good to very good agreement to MDCT. They significantly underestimated aortic annular area and circumference vs. MDCT with the auto method underestimating it the most and the manual method the least (6.5% and 1.3% respectively for area and circumference). For experts, the manual TEE method offered the least systematic bias while the SA method had narrower limits of agreement (LOA). For the novice operator, SA method provided the least bias and narrower LOA vs. MDCT. There is good agreement between novice and experts for all 3 TEE methods but better agreement with auto and SA methods as opposed to manual one. Our study supports the use of 3D-TEE as a complementary method to MDCT for aortic annular sizing. The newer auto and SA software, that requires minimal operator intervention, is an easy to use, reliable and reproducible tool for aortic annulus sizing for experienced operators, and especially less experienced ones.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| 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".