Artificial intelligence in gastrointestinal endoscopy - Rahul Pannala, MD, MPH, FASGE; Kumar Krishnan, MD; Joshua Melson, MD, MPH, FASGE; Mansour A. Parsi, MD, MPH, FASGE; Allison R. Schulman, MD, MPH; Shelby Sullivan, MD; Guru Trikudanathan, MBBS; Arvind J. Trindade, MD; Rabindra R. Watson, MD; John T. Maple, DO, FASGE; David R. Lichtenstein, MD, FASGE (VideoGIE, 2020)
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.