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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.
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http://dx.doi.org/10.1007/s11263-024-01996-x | DOI Listing |
Multimed Man Cardiothorac Surg
December 2024
Maria Fareri Children's Hospital, Westchester Medical Center, Valhalla, NY, USA.
A 2-week-old, 2.6-kg neonate without tuberous sclerosis presented with a severe right ventricular outflow tract obstruction secondary to a large mass. Transthoracic echocardiography revealed a maximum right ventricular outflow tract gradient of at least 95 mmHg.
View Article and Find Full Text PDFPediatr Surg Int
December 2024
Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.
Purpose: Congenital diaphragmatic hernia (CDH) is associated with congenital heart disease (CHD) and index newborns reportedly may experience cardiac arrhythmia disorders [Tella et al.-Pediatric Critical Care Medicine 2022]. This study analyses, details and reports contemporary outcome metrics of CHD and cardiac rhythm disease (CRD) in CDH babies attending a university surgical centre.
View Article and Find Full Text PDFEchocardiography
January 2025
Department of Pediatric Cardiology, Cerrahpasa Medical Faculty, Istanbul, Turkey.
Purpose: We presented the experience of a tertiary care center for maternal and fetal diseases and assessed the findings fetuses with double-inlet left ventricle (DILV) regarding fetal echocardiography, prenatal course including fetal growth and death, and postnatal outcome.
Methods: In this retrospective study, patients diagnosed with DILV via prenatal ultrasound in the maternal-fetal medicine department between 2015 and 2023 were included to evaluate important aspects of prenatal diagnosis and course, as well as postnatal management and outcome.
Results: There were 33 DILV cases prenatally diagnosed and postnatally confirmed.
Klin Padiatr
December 2024
Department of Neonatology, University Medical Centre Mannheim, Mannheim, Germany.
Background: Congenital heart defects (CHD) being the most common congenital malformation, significantly impact mortality and morbidity in children and adults. Early detection greatly improves treatment and prognosis. Routine pulse oximetry screening and fetal echocardiography in Germany have advanced early CHD diagnosis.
View Article and Find Full Text PDFPeerJ Comput Sci
November 2024
Department of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan.
Congenital heart disease (CHD) remains a significant global health challenge, particularly contributing to newborn mortality, with the highest rates observed in middle- and low-income countries due to limited healthcare resources. Machine learning (ML) presents a promising solution by developing predictive models that more accurately assess the risk of mortality associated with CHD. These ML-based models can help healthcare professionals identify high-risk infants and ensure timely and appropriate care.
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