Three-dimensional echocardiographic quantification of the left-heart chambers using an automated adaptive analytics algorithm: multicentre validation study
Why this work is in the frame
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Bibliographic record
Abstract
Aims: Although recommended by current guidelines, adoption of three-dimensional echocardiographic (3DE) chamber quantification in clinical practice has lagged because of time-consuming analysis. We recently validated an automated algorithm that measures left atrial (LA) and left ventricular (LV) volumes and ejection fraction (EF). This study aimed to determine the accuracy and reproducibility of these measurements in a multicentre setting. Methods and results: 180 patients underwent 3DE imaging (Philips) at six sites. Images were analysed using automated HeartModel (HM) software with endocardial border correction when necessary and by manual tracing. Measurements were performed by each site and by the Core Laboratory (CL) as the reference. Inter-technique comparisons included HM measurements by the sites against manual tracing by CL, and showed strong correlations (r-values: LVEDV: 0.97, LVESV: 0.97, LVEF: 0.88, LAV: 0.96), with the automated technique slightly underestimating LV volumes (biases: LVEDV: -14 ± 20 ml, LVESV: -6 ± 20 ml), LVEF (-2 ± 7%) and LAV (-9 ± 10 ml). Intra-technique comparisons included HM measurements by the sites against CL, with and without corrections. Corrections were unnecessary or minimal in most patients, and improved the measurements only modestly. Comparisons without corrections showed perfect agreement for all parameters. With corrections, correlations were better (r-values: LVEDV: 0.99, LVESV: 0.99, LVEF: 0.94, LAV: 0.99) and biases (LVEDV: -8 ± 12 ml, LVESV: -6 ± 12 ml, LVEF: 1 ± 5%, LAV: -10 ± 6 ml) smaller than in inter-technique comparison. All automated measurements with corrections were more reproducible than manual measurements. Conclusion: Automated 3DE analysis of left-heart chambers is an accurate alternative to conventional manual methodology, which yields almost the same values across laboratories and is more reproducible. This technique may contribute towards full integration of 3DE quantification into clinical routine.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it