Published online Jun 28, 2022. doi: 10.37126/aige.v3.i3.16
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.
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.