Ultrasound based mitral valve annulus tracking for off-pump beating heart mitral valve repair
Bibliographic record
Abstract
Mitral regurgitation (MR) occurs when the mitral valve cannot close properly during systole. The NeoChord© tool aims to repair MR by implanting artificial chordae tendineae on flail leaflets inside the beating heart, without a cardiopulmonary bypass. Image guidance is crucial for such a procedure due to the lack of direct vision of the targets or instruments. While this procedure is currently guided solely by transesophageal echocardiography (TEE), our previous work has demonstrated that guidance safety and efficiency can be significantly improved by employing augmented virtuality to provide virtual presentation of mitral valve annulus (MVA) and tools integrated with real time ultrasound image data. However, real-time mitral annulus tracking remains a challenge. In this paper, we describe an image-based approach to rapidly track MVA points on 2D/biplane TEE images. This approach is composed of two components: an image-based phasing component identifying images at optimal cardiac phases for tracking, and a registration component updating the coordinates of MVA points. Preliminary validation has been performed on porcine data with an average difference between manually and automatically identified MVA points of 2.5mm. Using a parallelized implementation, this approach is able to track the mitral valve at up to 10 images per second.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".