RESUMEN
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.
RESUMEN
In 2016, a group of 55 Syrian quota refugees arrived in Iceland from Lebanon and settled in three municipalities. There were 11 families comprising 20 adults and 35 children. This study1 aimed to critically explore the experiences, opportunities and challenges of these children, their parents, their teachers and principals in the municipalities of their resettlement since their arrival in Iceland. The theoretical framework of the study includes critical approaches to education, and multilingual education for social justice. Methods of data collection included semi-structured interviews with the refugee parents, the head teachers and teachers in all the schools in the study. While the findings indicate that most of the children were doing well both academically and socially in their first months in the schools, they also show that the children and parents have experienced a number of challenges. These included illiteracy, interrupted schooling of the children and hidden trauma before arriving in Iceland. After arrival, the parents have experienced lack of communication between schools and homes, as well as differences in norms, values, languages, and expectations between the schools and homes.