Abstract 10580: Machine Learning-Assisted Echocardiographic Identification of Children at High Risk for Treatment-Related Cardiomyopathy: A Proof-of-Concept Study

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.