Overview of Deep Learning in Gastrointestinal Endoscopy
Gut and Liver
; : 388-393, 2019.
Article
en En
| WPRIM
| ID: wpr-763862
Biblioteca responsable:
WPRO
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.
Palabras clave
Texto completo:
1
Índice:
WPRIM
Asunto principal:
Patología
/
Pólipos
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Neoplasias Gástricas
/
Ancylostomatoidea
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Inteligencia Artificial
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Enfermedad Celíaca
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Endoscopía del Sistema Digestivo
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Endoscopía Gastrointestinal
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Helicobacter pylori
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Diagnóstico por Computador
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Gut and Liver
Año:
2019
Tipo del documento:
Article