Interpretable Prediction of Pulmonary Hypertension in Newborns using Echocardiograms

TL;DR

This paper introduces an interpretable deep learning method using echocardiograms to accurately predict pulmonary hypertension in newborns, providing a promising automated diagnostic tool with clinically relevant insights.

Contribution

It presents the first automated, multi-view video-based deep learning approach for neonatal PH detection using echocardiograms, incorporating interpretability through saliency maps.

Findings

Achieved 0.84 F1-score for severity prediction

Achieved 0.92 F1-score for binary detection

Model focuses on clinically relevant cardiac structures

Abstract

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.