Intestinal parasitic infections are among important health problems in developing countries. In societies living in low socioeconomic conditions, it has been neglected and mostly affects children. It is important to determine the prevalence and type of intestinal parasites in order to determine the intervention strategies for these infections. Therefore, the aim of this study is to evaluate intestinal parasite prevalence and IgE levels and the factors associated with the region in which the children population live, in Sirnak province, in the eastern of Turkey. A total of 357 symptomatic children aged 4 to 12 years, who were admitted to the Paediatric Polyclinic of Sirnak State Hospital, were examined prospectively. The collected stool samples were examined with direct wet-mount and concentration method under light microscope. In addition, total serum IgE levels were compared among 223 children with parasitic disease and 134 children without parasitic disease. One or more intestinal parasites were detected in 223 out of the 357 children participating in the study. The ratio of single, double, and triple parasitic infections in children was 32.5 %, 22.4 % and 7.6 %, respectively. The most common parasites determined in the study were Taenia spp. (39.9%), Enterobius vermicularis (38.6%) and Giardia intestinalis. (30 %). The difference between IgE levels determined in both groups was not regarded to be statistically significant. This study indicated that that intestinal polyparism is very common in children living in the province of Sirnak, which is located in the east of Turkey, neighbouring Iraq and Syria in the South. For this reason, sustainable control measures are urgently needed to improve personal hygiene and sanitation, to provide a healthy infrastructure and to improve the quality of existing water resources.
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures.