Abstract 4368616: Core lab versus computer: Pediatric echocardiogram measurement agreement between expert human and AI readers
Bibliographic record
Abstract
Background: Deep learning algorithms for automated echocardiographic measurements have demonstrated strong performance in adult populations; however, their utility in pediatric echocardiography remains unclear. We evaluated the agreement between an FDA-approved software for automated adult echocardiogram measurements by Us2.ai and a pediatric core lab reader in assessing left ventricular (LV) size and function. Methods: We analyzed a retrospective dataset of pediatric echocardiogram DICOM files from 5 pediatric centers and corresponding core lab measurements collected from childhood cancer survivors under 21 years of age. The automated software processed the DICOM files, and agreement with core lab measurements for 17 2D and Doppler measurements was assessed using mean difference and intraclass correlation coefficient (ICC; two-way random effects, absolute agreement, single measures). Results: A total of 652 echocardiograms from 153 childhood cancer survivors were included. Median age at time of study was 13.4 (Q1 - Q3: 9.5 - 16.3) years, and 16% of studies showed depressed LV systolic function by core lab measurements (LV shortening fraction ≤28% or ejection fraction [EF] ≤50%). Table 1 summarizes the mean difference and ICC between the automated and core lab reader. Agreement was at least moderate (ICC > 0.5) across all variables. On average, the automated software underestimated biplane EF by 5 percentage points compared to the core lab reader with greater mean differences observed at higher EFs (-1 for core lab EF ≤ 50% and -5 for EF >50%; Figure 1). Conclusions: Independent validation of an automated echocardiographic measurement software in a pediatric dataset demonstrated at least moderate agreement of all measurements with gold-standard core lab measurements. The software exhibited a bias toward lower ejection fraction values; however, ICC for ejection fraction was comparable to previously reported interobserver variability among human pediatric readers.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".