  Pulmonary hypertension (PH) in newborns and infants is a complex condition
associated with several pulmonary, cardiac, and systemic diseases contributing
to morbidity and mortality. Therefore, accurate and early detection of PH is
crucial for successful management. Using echocardiography, the primary
diagnostic tool in pediatrics, human assessment is both time-consuming and
expertise-demanding, raising the need for an automated approach. In this work,
we present an interpretable multi-view video-based deep learning approach to
predict PH for a cohort of 194 newborns using echocardiograms. We use
spatio-temporal convolutional architectures for the prediction of PH from each
view, and aggregate the predictions of the different views using majority
voting. To the best of our knowledge, this is the first work for an automated
assessment of PH in newborns using echocardiograms. Our results show a mean
F1-score of 0.84 for severity prediction and 0.92 for binary detection using
10-fold cross-validation. We complement our predictions with saliency maps and
show that the learned model focuses on clinically relevant cardiac structures,
motivating its usage in clinical practice.
