Overview of Deep Learning in Gastrointestinal Endoscopy.
Capsule Endoscopy
Celiac Disease
/ diagnosis
Colonic Polyps
/ diagnosis
Colonoscopy
Deep Learning
Diagnosis, Computer-Assisted
Endoscopy, Digestive System
Endoscopy, Gastrointestinal
Gastroenterology
Gastrointestinal Motility
Helicobacter Infections
/ diagnosis
Hookworm Infections
/ diagnosis
Humans
Intestinal Polyps
/ diagnosis
Stomach Neoplasms
/ diagnosis
Artificial intelligence
Convolutional neural network
Deep learning
Diagnosis
Endoscopy
computer-assisted
Journal
Gut and liver
ISSN: 2005-1212
Titre abrégé: Gut Liver
Pays: Korea (South)
ID NLM: 101316452
Informations de publication
Date de publication:
11 01 2019
11 01 2019
Historique:
received:
29
08
2018
revised:
22
09
2018
accepted:
01
10
2018
pubmed:
12
1
2019
medline:
6
2
2020
entrez:
12
1
2019
Statut:
ppublish
Résumé
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,
Identifiants
pubmed: 30630221
pii: gnl18384
doi: 10.5009/gnl18384
pmc: PMC6622562
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
388-393Références
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