Deep Learning Based Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms
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11263_2024_Article_1996.pdf
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Author(s)
Ragnarsdottir, Hanna
Ozkan, Ece
Michel, Holger
Chin-Cheong, Kieran
Manduchi, Laura
Wellmann, Sven
Vogt, Julia E.
Date Issued
February 6, 2024
Publisher
Springer US
Citation
Ragnarsdottir, H., Ozkan, E., Michel, H. et al. Deep Learning Based Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms. Int J Comput Vis (2024).
Version
Final published version
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. Thus, accurate and early detection of PH and the classification of its severity is crucial for appropriate and 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. Little effort has been directed towards automatic assessment of PH using echocardiography, and the few proposed methods only focus on binary PH classification on the adult population. In this work, we present an explainable multi-view video-based deep learning approach to predict and classify the severity of PH for a cohort of 270 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. Our results show a mean F1-score of 0.84 for severity prediction and 0.92 for binary detection using 10-fold cross-validation and 0.63 for severity prediction and 0.78 for binary detection on the held-out test set. 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. To the best of our knowledge, this is the first work for an automated assessment of PH in newborns using echocardiograms.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1007/s11263-024-01996-x
