Three-dimensional echocardiography in a dynamic heart phantom: comparison of five different methods to measure chamber volume using a commercially available software
Bibliographic record
Abstract
Several methods of analysis are available for quantification of left ventricular volumes and ejection fraction using three-dimensional (3D) echocardiography. This study compared the accuracy and reproducibility of five methods of analysis in a novel, irregularly shaped dynamic heart phantom with excellent image quality. Five 3D datasets were acquired on a Philips IE33 platform using an X5-1 3D transducer. Each dataset was analysed by five different methods using the Philips QLab v8.1 software: Methods A1, A2 and A3, semi-automated contour detection with varying degrees of user correction; Method B, Simpson's biplane method using optimally aligned four- and two-chamber views and Method C, method of discs, manually delineated in reconstructed short-axis views. Time-volume curves were generated for each method and compared with the true volumes measured throughout systole in the phantom heart. A second observer repeated measurements by each method in a single 3D dataset. Method A1 (uncorrected semi-automated contouring) produced the most consistent time-volume curves, although end-diastolic and end-systolic volumes varied between datasets. Any manual correction of contours (Methods A2, A3 and B) resulted in significant variation in the time-volume curves, with less consistent endocardial tracking. Method C was not only the most accurate and reproducible method, but also the most time-consuming one. Different methods of 3D volume quantification vary significantly in accuracy and reproducibility using an irregular phantom heart model. Although contouring may appear optimal in long-axis views, this may not be replicated circumferentially, and the resulting measures appeared to be less robust following the manual correction of semi-automated contours.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".