Overview of Deep Learning in Gastrointestinal Endoscopy

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 t...

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Veröffentlicht in:Gut and liver Jg. 13; H. 4; S. 388 - 393
Hauptverfasser: Min, Jun Ki, Kwak, Min Seob, Cha, Jae Myung
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Korea (South) Editorial Office of Gut and Liver 11.01.2019
Gastroenterology Council for Gut and Liver
거트앤리버 소화기연관학회협의회
Schlagworte:
artificial intelligence
Capsule Endoscopy
Celiac Disease - diagnosis
Colonic Polyps - diagnosis
Colonoscopy
computer-assisted
convolutional neural network
Deep Learning
diagnosis
Diagnosis, Computer-Assisted
endoscopy
Endoscopy, Digestive System
Endoscopy, Gastrointestinal
Gastroenterology
Gastrointestinal Motility
Helicobacter Infections - diagnosis
Hookworm Infections - diagnosis
Humans
Intestinal Polyps - diagnosis
Review
Stomach Neoplasms - diagnosis
내과학
Deep learning
Diagnosis
Endoscopy
Artificial intelligence
Convolutional neural network
computer-assisted
ISSN:1976-2283, 2005-1212, 2005-1212
Online-Zugang:Volltext
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Zusammenfassung: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, 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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
ObjectType-Review-3
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ISSN:1976-2283
2005-1212
2005-1212
DOI:10.5009/gnl18384