Published online Jul 28, 2020. doi: 10.37126/aige.v1.i1.1
Peer-review started: June 1, 2020
First decision: June 18, 2020
Revised: July 14, 2020
Accepted: July 17, 2020
Article in press: July 17, 2020
Published online: July 28, 2020
The application of artificial intelligence (AI), especially machine learning or deep learning (DL), is advancing at a rapid pace. The need for increased accuracy at endoscopic visualisation of the gastrointestinal (GI) tract is also growing. Convolutional neural networks (CNNs) are one such model of DL, which have been used for endoscopic image analysis, whereby computer-aided detection and diagnosis of GI pathology can be carried out with increased scrupulousness. In this article, we briefly focus on the framework of the utilisation of CNNs in GI endoscopy along with a short review of a few published AI-based articles in the last 4 years.
Core tip: The convolutional neural network (CNN), a deep learning model, has gained immense success in endoscopy image analysis, with its application to diagnose and detect gastrointestinal (GI) pathology at endoscopy. This article shares a basic framework of the utilisation of CNNs in GI endoscopy, along with a concise review of a few published AI-based endoscopy articles in the last 4 years.