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Background and Aims: Esophageal function testing is an integral component of the evaluation of refractory GERD and esophageal motility disorders. This review summarizes the current technologies available for esophageal function testing, including the functional luminal imaging probe (FLIP), high-resolution esophageal manometry (HRM), and multichannel intraluminal impedance (MII) and pH monitoring. Methods: We performed a MEDLINE, PubMed, and MAUDE database literature search to identify pertinent clinical studies through March 2021 using the following key words: esophageal manometry, HRM, esophageal impedance, FLIP, MII, and esophageal pH testing. Technical data were gathered from traditional and web-based publications, proprietary publications, and informal communications with pertinent vendors. The report was drafted, reviewed, and edited by the American Society for Gastrointestinal Endoscopy Technology Committee and approved by the Governing Board of the American Society for Gastrointestinal Endoscopy. Results: FLIP is a high-resolution impedance planimetry system used for pressure and dimension measurement in the esophagus, pylorus, and anal sphincter. FLIP provides complementary information to HRM for esophageal motility disorders, especially achalasia. The Chicago classification, based on HRM data, is a widely adopted algorithmic scheme used to diagnose esophageal motility disorders. MII detects intraluminal bolus movement and, combined with pH measurement or manometry, provides information on acid and non-acid gastroesophageal reflux and bolus transit in patients with refractory GERD and for preoperative evaluation for anti-reflux procedures. Conclusions: Esophageal function testing techniques (FLIP, HRM, and MII-pH) have diagnostic and prognostic value in the evaluation of esophageal motility disorders and refractory GERD. Newer technologies and classification systems have enabled an increased understanding of these diseases.

Background and Aims: Artificial intelligence (AI)-based applications have transformed several industries and are widely used in various consumer products and services. In medicine, AI is primarily being used for image classification and natural language processing and has great potential to affect image-based specialties such as radiology, pathology, and gastroenterology (GE). This document reviews the reported applications of AI in GE, focusing on endoscopic image analysis. Methods: The MEDLINE database was searched through May 2020 for relevant articles by using key words such as machine learning, deep learning, artificial intelligence, computer-aided diagnosis, convolutional neural networks, GI endoscopy, and endoscopic image analysis. References and citations of the retrieved articles were also evaluated to identify pertinent studies. The manuscript was drafted by 2 authors and reviewed in person by members of the American Society for Gastrointestinal Endoscopy Technology Committee and subsequently by the American Society for Gastrointestinal Endoscopy Governing Board. Results: Deep learning techniques such as convolutional neural networks have been used in several areas of GI endoscopy, including colorectal polyp detection and classification, analysis of endoscopic images for diagnosis of Helicobacter pylori infection, detection and depth assessment of early gastric cancer, dysplasia in Barrett’s esophagus, and detection of various abnormalities in wireless capsule endoscopy images. Conclusions: The implementation of AI technologies across multiple GI endoscopic applications has the potential to transform clinical practice favorably and improve the efficiency and accuracy of current diagnostic methods.