The feasibility of wireless capsule endoscopy in detecting small intestinal pathology in children under the age of 8 years: a multicentre European study.

Objective: To systematically evaluate the feasibility and methodology to carry out wireless capsule endoscopy (WCE) in children <8 years to define small intestinal pathology. Design: Prospective European multicentre study with negative prior investigation. Patients and interventions: 83 children aged 1.5–7.9 years were recruited. Initially, all were offered “swallowing” (Group 1) for capsule introduction. If this failed endoscopic placement (Group 2) was used and the Roth net, Advance or custom-made introducers were compared. Outcome measures: Primary endpoint: to determine pathology; secondary endpoint: comparison of capsule introduction methods. Results: Capsule introduction: 20 (24%) children aged 4.0–7.9 years (mean, 6.9 years; 14 male) comprising Group 1 were older (p<0.025) than 63 (76%) aged 1.5–7.9 years (mean, 5.25 years; 30 male) forming Group 2. Complications: Roth net mucosal trauma in 50%; no others occurred. The available recording apparatus was inappropriate for those <3 years. Indications: gastrointestinal bleeding: n = 30 (16 positive findings: four ulcerative jejunitis, four polyps, two angiodysplasia, two blue rubber blebs, two Meckel’s diverticula, one anastomotic ulcer, one reduplication); suspected Crohn’s disease: n = 20 (11 had Crohn’s disease); abdominal pain: n = 12 (six positive findings: three Crohn’s disease, two lymphonodular hyperplasia, one blue rubber bleb); protein loss: n = 9 (four lymphangectasia); malabsorption: n = 12 (seven positive findings: six enteropathy, one ascaris). No abnormalities overall: 45%. Conclusion: WCE is feasible and safe down to the age of 1.5 years. 20 children >4 years swallowed the capsule. The Advance introducer proved superior for endoscopic placement. The pathologies encountered showed age specificity and, unlike in adolescents, obscure gastrointestinal bleeding was the commonest indication

Detecting and explaining unfairness in consumer contracts through memory networks

Recent work has demonstrated how data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents. However, a shortcoming of data-driven approaches is poor explainability. We posit that in this domain useful explanations of classifier outcomes can be provided by resorting to legal rationales. We thus consider several configurations of memory-augmented neural networks where rationales are given a special role in the modeling of context knowledge. Our results show that rationales not only contribute to improve the classification accuracy, but are also able to offer meaningful, natural language explanations of otherwise opaque classifier outcomes

Treatment of esophageal achalasia in children: Today and tomorrow

Esophageal achalasia (EA) is a rare esophageal motility disorder in children. Laparoscopic Heller myotomy (LHM) represents the treatment of choice in young patients. Peroral endoscopic myotomy (POEM) is becoming an alternative to LHM. The aim of this study is to evaluate the effectiveness, safety, and outcomes of POEM vs LHM in treatment of children with EA. Data of pediatric patients with EA, who underwent LHM and POEM from February 2009 to December 2013 in two centers, were collected. Eighteen patients (9 male, mean age: 11.6 years; range: 2-17 years) were included. Nine patients (6 male, mean age: 10.7 years; range: 2-16 years) underwent LHM, and the other 9 (3 males, mean age: 12.2 years; range: 6-17 years) underwent POEM procedure. Mean operation time was shorter in POEM group compared with LHM group (62/149 minutes). Myotomy was longer in POEM group than in LHM group (11/7 cm). One major complication occurred after LHM (esophageal perforation). No clinical and manometric differences were observed between LHM and POEM in follow-up. The incidence of iatrogenic gastroesophageal reflux disease was low (1 patient in both groups). Results of a midterm follow-up show that LHM and POEM are safe and effective treatments also in children. Besides, POEM is a mini-invasive technique with an inferior execution timing compared to LHM. A skilled endoscopic team is mandatory to perform this procedur

Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning

Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks. In this position paper, we posit that neural-symbolic and statistical relational learning could play a crucial role in the integration of symbolic and sub-symbolic methods to achieve this goal

Explaining potentially unfair clauses to the consumer with the claudette tool

This paper presents the latest developments of the use of memory network models in detecting and explaining unfair terms in online consumer contracts. We extend the CLAUDETTE tool for the detection of potentially unfair clauses in online Terms of Service, by providing to the users the explanations of unfairness (legal rationales) for five different categories: Arbitration, unilateral change, content removal, unilateral termination, and limitation of liability

Deep learning for detecting and explaining unfairness in consumer contracts

Consumer contracts often contain unfair clauses, in apparent violation of the relevant legislation. In this paper we present a new methodology for evaluating such clauses in online Terms of Services. We expand a set of tagged documents (terms of service), with a structured corpus where unfair clauses are liked to a knowledge base of rationales for unfairness, and experiment with machine learning methods on this expanded training set. Our experimental study is based on deep neural networks that aim to combine learning and reasoning tasks, one major example being Memory Networks. Preliminary results show that this approach may not only provide reasons and explanations to the user, but also enhance the automated detection of unfair clauses