Overview of Deep Learning in Gastrointestinal Endoscopy.


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
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

388-393

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Auteurs

Jun Ki Min (JK)

Department of Internal Medicine, Kyung Hee University School of Medicine, Seoul, Korea.

Min Seob Kwak (MS)

Department of Internal Medicine, Kyung Hee University School of Medicine, Seoul, Korea.

Jae Myung Cha (JM)

Department of Internal Medicine, Kyung Hee University School of Medicine, Seoul, Korea.

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Classifications MeSH