Background:Intracranial pressure (ICP) monitoring is important in many neurosurgical and neurological patients. The gold standard for monitoring ICP, however, is via an invasive procedure resulting in the placement of an intraventricular catheter, which is associated with many risks. Several noninvasive ICP monitoring techniques have been examined with the hope to replace the invasive techniques. The goal of this paper is to provide an overview of all modalities that have been used for noninvasive ICP monitoring to date.Methods:A thorough literature search was conducted on PubMed, selected articles were reviewed in completion, and pertinent data was included in the review.Results:A total of 94 publications were reviewed, and we found that over the past few decades clinicians have attempted to use a number of modalities to monitor ICP noninvasively.Conclusion:Although the intraventricular catheter remains the gold standard for monitoring ICP, several noninvasive modalities that can be used in settings when invasive monitoring is not possible are also available. In our opinion, measurement of optic nerve sheath diameter and pupillometry are the two modalities which may prove to be valid options for centers not performing invasive ICP monitoring.
Background Environmental Enteropathy (EE), characterized by alterations in intestinal structure, function, and immune activation, is believed to be an important contributor to childhood undernutrition and its associated morbidities, including stunting. Half of all global deaths in children < 5 years are attributable to under-nutrition, making the study of EE an area of critical priority. Methods Community based intervention study, divided into two sub-studies, 1) Longitudinal analyses and 2) Biopsy studies for identification of EE features via omics analyses. Birth cohorts in Matiari, Pakistan established: moderately or severely malnourished (weight for height Z score (WHZ) < − 2) children, and well-nourished (WHZ > 0) children. Blood, urine, and fecal samples, for evaluation of potential biomarkers, will be collected at various time points from all participants (longitudinal analyses). Participants will receive appropriate educational and nutritional interventions; non-responders will undergo further evaluation to determine eligibility for further workup, including upper gastrointestinal endoscopy. Histopathological changes in duodenal biopsies will be compared with duodenal biopsies obtained from USA controls who have celiac disease, Crohn’s disease, or who were found to have normal histopathology. RNA-Seq will be employed to characterize mucosal gene expression across groups. Duodenal biopsies, luminal aspirates from the duodenum, and fecal samples will be analyzed to define microbial community composition (omic analyses). The relationship between histopathology, mucosal gene expression, and community configuration will be assessed using a variety of bioinformatic tools to gain better understanding of disease pathogenesis and to identify mechanism-based biomarkers. Ethical review committees at all collaborating institutions have approved this study. All results will be made available to the scientific community. Discussion Operational and ethical constraints for safely obtaining intestinal biopsies from children in resource-poor settings have led to a paucity of human tissue-based investigations to understand and reverse EE in vulnerable populations. Furthermore, EE biomarkers have rarely been correlated with gold standard histopathological confirmation. The Study of Environmental Enteropathy and Malnutrition (SEEM) is designed to better understand the pathophysiology, predictors, biomarkers, and potential management strategies of EE to inform strategies to eradicate this debilitating pathology and accelerate progress towards the 2030 Sustainable Development Goals. Trial registration Retrospectively registered; clinicaltrials.gov ID NCT03588013 . Electronic supplementary material The online version of this article (10.1186/s12887-019-1564-x) contains supplementary material, which is available to authorized users.
Key Points Question Can deep learning image analysis distinguish pathological vs healthy features in duodenal tissue? Findings In this diagnostic study, a deep learning convolutional neural network was trained on 3118 images from duodenal biopsies of patients with environmental enteropathy, celiac disease, and no disease. The convolutional neural network achieved 93.4% case-detection accuracy, with a false-negative rate of 2.4%, and automatically learned microlevel features in duodenal tissue, such as alterations in secretory cell populations. Meaning A machine learning–based feature detection platform distinguished pathological from healthy tissue in gastrointestinal biopsy images.
Celiac Disease (CD) is a chronic autoimmune disease that affects the small intestine in genetically predisposed children and adults. Gluten exposure triggers an inflammatory cascade which leads to compromised intestinal barrier function. If this enteropathy is unrecognized, this can lead to anemia, decreased bone density, and, in longstanding cases, intestinal cancer. The prevalence of the disorder is 1% in the United States. An intestinal (duodenal) biopsy is considered the "gold standard" for diagnosis. The mild CD might go unnoticed due to non-specific clinical symptoms or mild histologic features. In our current work, we trained a model based on deep residual networks to diagnose CD severity using a histological scoring system called the modified Marsh score. The proposed model was evaluated using an independent set of 120 whole slide images from 15 CD patients and achieved an AUC greater than 0.96 in all classes. These results demonstrate the diagnostic power of the proposed model for CD severity classification using histological images.
Artificial intelligence (AI), a discipline encompassed by data science, has seen recent rapid growth in its application to healthcare and beyond, and is now an integral part of daily life. Uses of AI in gastroenterology include the automated detection of disease and differentiation of pathology subtypes and disease severity. Although a majority of AI research in gastroenterology focuses on adult applications, there are a number of pediatric pathologies that could benefit from more research. As new and improved diagnostic tools become available and more information is retrieved from them, AI could provide physicians a method to distill enormous amounts of data into enhanced decision-making and cost saving for children with digestive disorders. This review provides a broad overview of AI and examples of its possible applications in pediatric gastroenterology.
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