Abstract 10580: Machine Learning-Assisted Echocardiographic Identification of Children at High Risk for Treatment-Related Cardiomyopathy: A Proof-of-Concept Study
Bibliographic record
Abstract
Introduction: Childhood cancer survivors require life-long surveillance for treatment-related cardiomyopathy/heart failure (CHF). Ultimately, we aim to use machine learning to aid in echocardiographic discrimination of survivors more likely to develop future CHF. For this feasibility pilot, we built two proof-of-concept deep convolutional neural networks (DCNNs) tasked with binary classification of present/future CHF versus no CHF with the aim of assessing optimal data input format and model architecture. Methods: From a robust multi-institutional surveillance echo dataset, we selected pilot data comprising 171 parasternal short axis echo clips, 52 from 5 CHF+ (present and future CHF) and 119 from 21 CHF- patients. We built two DCNN models differing in frame selection for input data (Fig. 1), both tasked with binary classification of CHF+ vs. CHF-. We used holdout subsets in a 10-fold cross-validation framework to test model performance and Student’s t test (paired) to compare model performance. Results: Classification performance was similar, with mean AUROC values of 0.59 ± 0.09 and 0.53 ± 0.11 (p=0.14) for the models trained on Type I and Type II montages, respectively (Fig. 2). Conclusions: The DCNN framework is feasible for a model tasked with classifying CHF status, and we are optimistic that a similar model can be trained with pre-CHF diagnosis (case) vs control patient images to facilitate machine learning-assisted identification of childhood cancer survivors more likely to develop CHF in the future.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".