Comparing a Knowledge-based 3D Reconstruction Algorithm to TomTec 3D Echocardiogram Algorithm in Measuring Left Cardiac Chamber Volumes in the Pediatric population
Bibliographic record
Abstract
Background : Three-dimensional echocardiography (3DE) is an emerging method for volumetric cardiac measurements; however, few vendor-neutral analysis packages exist. Ventripoint Medical System Plus (VMS3.0+) proprietary software utilizes a validated MRI database of normal ventricular and atrial morphologies to calculate chamber volumes. This study aimed to compare left ventricular (LV) and atrial (LA) volumes obtained using VMS3.0+ to Tomtec echocardiography analysis software. Methods : Healthy controls (n=98) aged 0 to 18 years were prospectively recruited and 3D DICOM datasets focused on the LV and LA acquired. LV and LA volumes and ejection fractions were measured using TomTec Image Arena 3D LV analysis package and using VMS3.0+. Pearson correlation coefficients, Bland-Altman’s plots and intraclass coefficients (ICC) were calculated, along with analysis time. Results : There was a very good correlation between VMS and Tomtec LV systolic (r 2 = 0.88, ICC 0.89 [95% CI 0.81,0.94]), and diastolic (r 2 = 0.88, ICC 0.90 [95% CI 0.77,0.95]) volumes, and between VMS and Tomtec LA diastolic (r 2 =0.75, ICC 0.89 [95% CI 0.81,0.93]) and systolic (r 2 =0.88, ICC 0.91 [95% CI 0.78,0.96]) volumes on linear regression models. Natural log transformations eliminated heteroscedasticity, and power transformations provided best fit. The time (mins) to analyze volumes using VMS were less than using Tomtec (LV VMS 2.3±0.5, Tomtec 3.3±0.8, p<0.001; LA: VMS 1.9±0.4, Tomtec 3.4±1.0, p<0.001). Conclusions : There was very good correlation between knowledge-based (VMS3.0+) and 3D (Tomtec) algorithms when measuring 3D echocardiography derived LA and LV volumes in pediatric patients. VMS was slightly faster than Tomtec in analyzing volumetric measurements.
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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 | 0.003 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".