Impact of three-dimensional echocardiography in valvular heart disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Recent advances in the field of three-dimensional (3D) echocardiography have allowed improved visualization of cardiac structures. These advances have also provided valuable insights into cardiac function. The purpose of this review is to describe the recent developments in 3D echocardiography in assessing valvular heart disease. RECENT FINDINGS: Application of 3D echocardiography to valvular heart disease has improved with advances made in both the hardware and software components of 3D ultrasound systems. The most significant advancement has been the development of a matrix transducer that is capable of rapid real-time 3D acquisition and rendering. There have been many studies evaluating 3D echocardiographic assessment of mitral valve disease, aortic valve disease, as well as congenital heart disease using both real-time 3D transthoracic echocardiography (TTE) as well as off-line reconstructed 3D images from transesophageal echocardiography (TEE) using post image processing. More recent studies have combined the structural 3D information with color Doppler 3D imaging, providing qualitative functional information. SUMMARY: Developments in the field of 3D ultrasound imaging have allowed better qualitative assessment of valvular structures. The addition of color flow Doppler to the 3D imaging has provided improved visualization of regurgitant lesions and holds great promise for improved quantitative assessment of such lesions. The ongoing miniaturization of transducers and improvements in hardware and software components of ultrasound systems will certainly enhance both the ease of image acquisition as well as image quality, which should result in more precise quantitation of valvular dysfunction. However, clinical benefits of 3D echocardiography are yet to be demonstrated in properly conducted clinical trials, which are needed for wider acceptance of this technique.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".