Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Gastrointest Endosc. Jun 28, 2022; 3(3): 16-30
Published online Jun 28, 2022. doi: 10.37126/aige.v3.i3.16
Artificial intelligence in endoscopy: More than what meets the eye in screening colonoscopy and endosonographic evaluation of pancreatic lesions
Harshavardhan Rao B, Judy A Trieu, Priya Nair, Gilad Gressel, Mukund Venu, Rama P Venu
Harshavardhan Rao B, Priya Nair, Rama P Venu, Department of Gastroenterology, Amrita Institute of Medical Sciences, Kochi 682041, Kerala, India
Judy A Trieu, Mukund Venu, Internal Medicine - Gastroenterology, Loyola University Medical Center, Maywood, IL 60153, United States
Gilad Gressel, Center for Cyber Security Systems and Networks, Amrita Vishwavidyapeetham, Kollam 690546, Kerala, India
Author contributions: Rao B H, Trieu JA, and Nair P performed the majority of the writing, prepared the figures and tables; Gressel G provided valuable inputs in the technical aspects of Artificial intelligence and data science; Venu M and Venu RP designed the outline and coordinated the writing of the paper.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Rama P Venu, AGAF, FACG, FACP, FASGE, MD, Emeritus Professor, Department of Gastroenterology, Amrita Institute of Medical Sciences, Amrita University, Kochi 682041, India.
Received: December 30, 2021
Peer-review started: December 30, 2021
First decision: February 21, 2022
Revised: March 7, 2022
Accepted: May 7, 2022
Article in press: May 7, 2022
Published online: June 28, 2022

Artificial intelligence (AI)-based tools have ushered in a new era of innovation in the field of gastrointestinal (GI) endoscopy. Despite vast improvements in endoscopic techniques and equipment, diagnostic endoscopy remains heavily operator-dependent, in particular, colonoscopy and endoscopic ultrasound (EUS). Recent reports have shown that as much as 25% of colonic adenomas may be missed at colonoscopy. This can result in an increased incidence of interval colon cancer. Similarly, EUS has been shown to have high inter-observer variability, overlap in diagnoses with a relatively low specificity for pancreatic lesions. Our understanding of Machine-learning (ML) techniques in AI have evolved over the last decade and its application in AI–based tools for endoscopic detection and diagnosis is being actively investigated at several centers. ML is an aspect of AI that is based on neural networks, and is widely used for image classification, object detection, and semantic segmentation which are key functional aspects of AI-related computer aided diagnostic systems. In this review, current status and limitations of ML, specifically for adenoma detection and endosonographic diagnosis of pancreatic lesions, will be summarized from existing literature. This will help to better understand its role as viewed through the prism of real world application in the field of GI endoscopy.

Keywords: Artificial intelligence, Artificial, Machine, Colonoscopy, Polyp, Endosonography, Pancreas

Core Tip: The influence of artificial intelligence (AI) based applications in our everyday practice as endoscopists has been steadily increasing. One of the areas where it has shown promise is in image discrimination and diagnosis, which has many applications in endoscopy. The increasing application and rapid advancement of technology in this area necessitates an understanding of the basics and scope of AI in gastroenterology. In this review, a brief technical basis of AI in image discrimination has been described, followed by an update on the role of AI in the prevention of colorectal cancer and the evaluation of specific pancreatic lesions using endoscopic ultrasound.