The Use of 3D Echocardiography in Surgical Planning of the Mitral Valve in Pediatric Cardiology
Bibliographic record
Abstract
Mitral valve disease in pediatric cardiology is complex and can involve a combination of annular, leaflet, chordae tendineae and papillary muscle abnormalities. Transthoracic two-dimensional echocardiography (2DE) remains the primary diagnostic imaging technique utilized in pediatric surgical planning. However, given that the mitral valve is a three-dimensional (3D) structure, the addition of 3D echocardiography (3DE) to better define the mechanisms of stenosis and/or regurgitation is advantageous. Transthoracic 3DE technology has improved with advances in probe technology and ultrasound scanners, producing images with good spatial resolution and adequate temporal resolution. Specifically, the addition of pediatric 3D transducers with higher frequencies and a smaller footprint provides better 3DE imaging in children. Improved efficiency of 3DE acquisition and analysis allow 3D assessment of the mitral valve to be more easily integrated by the sonographer, the cardiologist and the surgeon in mitral valve assessment. This improvement was also made possible by the postprocessing software optimization. In this method paper, we aim to describe the transthoracic 3DE assessment of the mitral valve in children and its use in the surgical planning of pediatric mitral valve disease. Firstly, the 3DE assessment begins by selecting the correct probe and by obtaining a view of the mitral valve. Then, the appropriate data acquisition method should be selected based on the individual patient. Next, optimization of the data set is critical in order to properly balance spatial and temporal resolution. During live scanning or following acquisition, the data set can be cropped using innovative tools that allow the user to quickly obtain an infinite number of cut planes or volumetric reconstructions. The cardiologist and surgeon can view the mitral valve en face; thus, accurately reconstructing its morphology in order to support medical or surgical planning. Finally, a review of some clinical applications is proposed, showing examples in pediatric mitral valve managements.
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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) | 0.003 | 0.012 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".