From a pure motor disorder of the bowel, in the past few years, irritable bowel syndrome (IBS) has become a multifactorial disease that implies visceral hypersensitivity, alterations at the level of nervous and humoral communications between the enteric nervous system and the central nervous system, alteration of the gut microflora, an increased intestinal permeability and minimum intestinal inflammation. Psychological and social factors can interfere with the communication between the central and enteric nervous systems, and there is proof that they are involved in the onset of IBS and influence the response to treatment and outcome. There is evidence that abuse history and stressful life events are involved in the onset of functional gastrointestinal disorders. In order to explain clustering of IBS in families, genetic factors and social learning mechanisms have been proposed. The psychological features, such as anxiety, depression as well as the comorbid psychiatric disorders, health beliefs and coping of patients with IBS are discussed in relation to the symptoms and outcome.

Gastroesophageal reflux disease is a chronic condition with a high prevalence in western countries. Transient lower esophageal sphincter relaxation episodes and a decreased lower esophageal sphincter pressure are the main mechanisms involved. Currently used drugs are efficient on reflux symptoms, but only as long as they are administered, because they do not modify the reflux barrier. Certain nutrients or foods are generally considered to increase the frequency of gastroesophageal reflux symptoms, therefore physicians recommend changes in diet and some patients avoid bothering foods. This review summarizes current knowledge regarding food and gastroesophageal reflux. For example, fat intake increases the perception of reflux symptoms. Regular coffee and chocolate induce gastroesophageal reflux and increase the lower esophageal exposure to acid. Spicy foods might induce heartburn, but the exact mechanism is not known. Beer and wine induce gastroesophageal reflux, mainly in the first hour after intake. For other foods, like fried food or carbonated beverages data on gastroesophageal reflux is scarce. Similarly, there are few data about the type of diet and gastroesophageal reflux. Mediterranean diet and a very low carbohydrate diet protect against reflux. Regarding diet-related practices, consistent data showed that a “short-meal-to-sleep interval” favors reflux episodes, therefore some authors recommend that dinner should be at least four hours before bedtime. All these recommendations should consider patient’s weight, because several meta-analyses showed a positive association between increased body mass index and gastroesophageal reflux disease.

Patients with heartburn and normal upper gastrointestinal endoscopy, normal oesophageal acid exposure, no symptom-reflux association and who fail to respond to a proton-pump inhibitor are classified as having functional heartburn (FH). This study aimed (i) to characterize the symptoms and functional abnormalities of patients with FH and (ii) to describe their clinical outcome. Among all patients referred for 24 h multichannel intraluminal impedance-pH (MII-pH), patients with FH were identified. The clinical characteristics and high-resolution oesophageal pressure topography recordings of FH patients were analyzed at the time of the 24-h MII-pH test. A symptom-related and health-related quality-of-life questionnaire was then sent to FH patients to assess the long-term outcome. Forty patients fulfilled the criteria for FH, representing 8.5% of the referred population. Twenty-two months after initial testing, 66% of patients still suffered from heartburn. The rate of mixed reflux (liquid/gas) was higher in patients with persisting heartburn at the final evaluation (63 vs. 50%, P=0.04). Sixty-six per cent of patients had one or more manometric abnormalities. Acid clearance time in MII-pH was significantly higher in patients with weak peristalsis than patients with normal peristalsis (60 ± 45 vs. 31 ± 19 s, P=0.03). A high rate of mixed reflux and/or a manometric abnormality were associated with a higher risk of persistent heartburn. FH is a chronic disorder with persisting symptoms in two-thirds of patients. An increased rate of mixed reflux and/or the presence of manometric abnormalities are associated with a higher risk of persisting symptoms and may help to identify the population with unmet therapeutic needs.

Eosinophilic esophagitis (EoE) is an eosinophil-rich, Th2 antigen-mediated disease of increasing worldwideprevalence. Originally considered common in children and young adults, it can be seen at any age, withthe highest prevalence between 30 and 40 years. Symptoms reflect esophageal dysfunction, and typicalendoscopic pictures consist of rings, furrows, exudates and edema. Progressive disease leads to pathologictissue remodeling, with ensuing esophageal rigidity and loss of luminal diameter caused by strictures. Thedefinitive diagnosis is histological (at least 15 eosinophils/HPF, high power field), upper gastrointestinalendoscopy with multiple esophageal biopsies being mandatory. Current therapeutic options include dietaryand pharmacologic treatments. Despite being successful in a high proportion of patients, elemental diet hasmultiple disadvantages. Therefore, a step-up approach (using a two-, four- and six food elimination diets) ispreferred, being globally effective in up to 79% of cases and avoiding unnecessary restrictions. Drug therapyrelies on proton pump inhibitors and topical corticosteroids. Esophageal dilation may be required to increaseluminal patency, leading to immediate symptomatic improvement in 95% of EoE patients, who have stricturesor narrow caliber esophagus. The chronic nature of the disease necessitates long-term therapy. In this review,current diagnostic and treatment options are discussed and a treatment algorithm is proposed.

Background and Aims: High-resolution esophageal manometry (HREM) is the gold standard procedure used for the diagnosis of esophageal motility disorders (EMD). Artificial intelligence (AI) might provide an efficient solution for the automatic diagnosis of EMD by improving the subjective interpretation of HREM images. The aim of our study was to develop an AI-based system, using neural networks, for the automatic diagnosis of HREM images, based on one wet swallow raw image. Methods: In the first phase of the study, the manometry recordings of our patients were retrospectively analyzed by three experienced gastroenterologists, to verify and confirm the correct diagnosis. In the second phase of the study raw images were used to train an artificial neural network. We selected only those tracings with ten test swallows that were available for analysis, including a total of 1570 images. We had 10 diagnosis categories, as follows: normal, type I achalasia, type II achalasia, type III achalasia, esophago-gastric junction outflow obstruction, jackhammer oesophagus, absent contractility, distal esophageal spasm, ineffective esophageal motility, and fragmented peristalsis, based on Chicago classification v3.0 for EMDs. Results: The raw images were cropped, binarized, and automatically divided in 3 parts: training, testing, validation. We used Inception V3 CNN model, pre-trained on ImageNet. We developed a custom classification layer, that allowed the CNN to classify each wet swallow image from the HREM system into one of the diagnosis categories mentioned above. Our algorithm was highly accurate, with an overall precision of more than 93%. Conclusion: Our neural network approach using HREM images resulted in a high accuracy automatic diagnosis of EMDs.

The goal of this paper is to provide a Machine Learning-based solution that can be utilized to automate the Chicago Classification algorithm, the state-of-the-art scheme for esophageal motility disease identification. First, the photos were preprocessed by locating the area of interest—the precise instant of swallowing. After resizing and rescaling the photos, they were utilized as input for the Deep Learning models. The InceptionV3 Deep Learning model was used to identify the precise class of the IRP. We used the DenseNet201 CNN architecture to classify the images into 5 different classes of swallowing disorders. Finally, we combined the results of the two trained ML models to automate the Chicago Classification algorithm. With this solution we obtained a top-1 accuracy and f1-score of 86% with no human intervention, automating the whole flow, from image preprocessing until Chicago classification and diagnosis.