Aim: Abdominal pain is a frequent reason for paediatric emergency department visits, but specific research is lacking. Our aim was to obtain information on the diagnosis of abdominal pain and what healthcare services children with this condition need. Methods: This retrospective study focused on patients visiting the emergency department of the Children's Hospital Iceland in 2010 with abdominal pain and any subsequent visits up to 1 January 2015.Results: There were 11 340 visits to the emergency department in 2010 and 1118 children made 1414 (12%) visits due to abdominal pain. The majority (58%) with abdominal pain were girls (p < 0.001) and they were older than the boys, with an average age of 12 versus 10 years (p < 0.001). The most common diagnoses were non-specific abdominal pain (40%), constipation (22%) and viral infections (13%). During the followup period, 423/1118 children (38%) visited the emergency department 883 times, 58% were girls and the most common diagnosis was non-specific abdominal pain (37%). Of the 436 children initially diagnosed with non-specific abdominal pain, 154 (35%) revisited the emergency department during the follow-up period.Conclusion: Abdominal pain was a common reason for visits to the paediatric emergency room and a third paid more than one visit.

No abstract

No abstract

Age-related changes in brain structure include atrophy of the brain parenchyma and white matter changes of presumed vascular origin. Enlargement of the ventricles may occur due to atrophy or impaired cerebrospinal fluid (CSF) circulation. The co-occurrence of these changes in neurodegenerative diseases and in aging brains often requires investigators to take both into account when studying the brain, however, automated segmentation of enlarged ventricles and white matter hyperintensities (WMHs) can be a challenging task. Here, we present a hybrid multi-atlas segmentation and convolutional autoencoder approach for joint ventricle parcellation and WMH segmentation from magnetic resonance images (MRIs). Our fully automated approach uses a convolutional autoencoder to generate a standardized image of grey matter, white matter, CSF, and WMHs, which, in conjunction with labels generated by a multi-atlas segmentation approach, is then fed into a convolutional neural network to parcellate the ventricular system. Hence, our approach does not depend on manually delineated training data for new data sets. The segmentation pipeline was validated on both healthy elderly subjects and subjects with normal pressure hydrocephalus using ground truth manual labels and compared with state-of-the-art segmentation methods. We then applied the method to a cohort of 2401 elderly brains to investigate associations of ventricle volume and WMH load with various demographics and clinical biomarkers, using a multiple regression model. Our results indicate that the ventricle volume and WMH load are both highly variable in a cohort of elderly subjects and there is an independent association between the two, which highlights the importance of taking both the possibility of enlarged ventricles and WMHs into account when studying the aging brain.