Overview of Deep Learning in Gastrointestinal Endoscopy
Gut and Liver
;
: 388-393, 2019.
Article
Dans Anglais
| WPRIM
| ID: wpr-763862
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.
Texte intégral:
Disponible
Indice:
WPRIM (Pacifique occidental)
Sujet Principal:
Anatomopathologie
/
Polypes
/
Tumeurs de l'estomac
/
Ancylostomatoidea
/
Intelligence artificielle
/
Maladie coeliaque
/
Endoscopie digestive
/
Endoscopie gastrointestinale
/
Helicobacter pylori
/
Diagnostic assisté par ordinateur
Type d'étude:
Etude diagnostique
/
Étude pronostique
Limites du sujet:
Humains
langue:
Anglais
Texte intégral:
Gut and Liver
Année:
2019
Type:
Article
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