Overview of Deep Learning in Gastrointestinal Endoscopy
- Author:
Jun Ki MIN
1
;
Min Seob KWAK
;
Jae Myung CHA
Author Information
- Publication Type:Review
- Keywords: Artificial intelligence; Convolutional neural network; Deep learning; Diagnosis, computer-assisted; Endoscopy
- MeSH: Ancylostomatoidea; Artificial Intelligence; Capsule Endoscopy; Celiac Disease; Colonoscopy; Diagnosis; Diagnosis, Computer-Assisted; Endoscopy; Endoscopy, Digestive System; Endoscopy, Gastrointestinal; Gastroenterology; Helicobacter pylori; Humans; Intestine, Small; Learning; Methods; Neural Networks (Computer); Pathology; Polyps; Stomach Neoplasms
- From:Gut and Liver 2019;13(4):388-393
- CountryRepublic of Korea
- Language:English
- Abstract: Artificial intelligence is likely to perform several roles currently performed by humans, and the adoption of artificial intelligence-based medicine in gastroenterology practice is expected in the near future. Medical image-based diagnoses, such as pathology, radiology, and endoscopy, are expected to be the first in the medical field to be affected by artificial intelligence. A convolutional neural network, a kind of deep-learning method with multilayer perceptrons designed to use minimal preprocessing, was recently reported as being highly beneficial in the field of endoscopy, including esophagogastroduodenoscopy, colonoscopy, and capsule endoscopy. A convolutional neural network-based diagnostic program was challenged to recognize anatomical locations in esophagogastroduodenoscopy images, Helicobacter pylori infection, and gastric cancer for esophagogastroduodenoscopy; to detect and classify colorectal polyps; to recognize celiac disease and hookworm; and to perform small intestine motility characterization of capsule endoscopy images. Artificial intelligence is expected to help endoscopists provide a more accurate diagnosis by automatically detecting and classifying lesions; therefore, it is essential that endoscopists focus on this novel technology. In this review, we describe the effects of artificial intelligence on gastroenterology with a special focus on automatic diagnosis, based on endoscopic findings.
