Artificial intelligence in gastrointestinal endoscopy

Abstract

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.

Keywords: ADR, adenoma detection rate; AI, artificial intelligence; AMR, adenoma miss rate; ANN, artificial neural network; BE, Barrett’s esophagus; CAD, computer-aided diagnosis; CADe, CAD studies for colon polyp detection; CADx, CAD studies for colon polyp classification; CI, confidence interval; CNN, convolutional neural network; CRC, colorectal cancer; DL, deep learning; GI, gastroenterology; HD-WLE, high-definition white light endoscopy; HDWL, high-definition white light; ML, machine learning; NBI, narrow-band imaging; NPV, negative predictive value; PIVI, preservation and Incorporation of Valuable Endoscopic Innovations; SVM, support vector machine; VLE, volumetric laser endomicroscopy; WCE, wireless capsule endoscopy; WL, white light.

Figure 1

Diagram representation of hierarchy of artificial intelligence domains (adapted from Goodfellow et al with permission). Abbreviations: AI, artificial intelligence; ML, machine learning; RL, representation learning; DL, deep learning.

Figure 2

Flowchart and descriptions of various types of learning and differentiation between conventional machine learning and deep learning (adapted from Chartrand et al with permission).

Figure 3

An example of convolutional neural network for colorectal polyps (adapted from Byrne et al with permission).