Artificial intelligence in gastrointestinal endoscopy.
ADR, adenoma detection rate
AI, artificial intelligence
AMR, adenoma miss rate
ANN, artificial neural network
BE, Barrett’s esophagus
CAD, computer-aided diagnosis
CADe, CAD studies for colon polyp detection
CADx, CAD studies for colon polyp classification
CI, confidence interval
CNN, convolutional neural network
CRC, colorectal cancer
DL, deep learning
GI, gastroenterology
HD-WLE, high-definition white light endoscopy
HDWL, high-definition white light
ML, machine learning
NBI, narrow-band imaging
NPV, negative predictive value
PIVI, preservation and Incorporation of Valuable Endoscopic Innovations
SVM, support vector machine
VLE, volumetric laser endomicroscopy
WCE, wireless capsule endoscopy
WL, white light
Journal
VideoGIE : an official video journal of the American Society for Gastrointestinal Endoscopy
ISSN: 2468-4481
Titre abrégé: VideoGIE
Pays: United States
ID NLM: 101719677
Informations de publication
Date de publication:
Dec 2020
Dec 2020
Historique:
entrez:
15
12
2020
pubmed:
16
12
2020
medline:
16
12
2020
Statut:
epublish
Résumé
Artificial intelligence (AI)-based applications have transformed several industries and are widely used in various consumer products and services. In medicine, AI is primarily being used for image classification and natural language processing and has great potential to affect image-based specialties such as radiology, pathology, and gastroenterology (GE). This document reviews the reported applications of AI in GE, focusing on endoscopic image analysis. The MEDLINE database was searched through May 2020 for relevant articles by using key words such as machine learning, deep learning, artificial intelligence, computer-aided diagnosis, convolutional neural networks, GI endoscopy, and endoscopic image analysis. References and citations of the retrieved articles were also evaluated to identify pertinent studies. The manuscript was drafted by 2 authors and reviewed in person by members of the American Society for Gastrointestinal Endoscopy Technology Committee and subsequently by the American Society for Gastrointestinal Endoscopy Governing Board. Deep learning techniques such as convolutional neural networks have been used in several areas of GI endoscopy, including colorectal polyp detection and classification, analysis of endoscopic images for diagnosis of The implementation of AI technologies across multiple GI endoscopic applications has the potential to transform clinical practice favorably and improve the efficiency and accuracy of current diagnostic methods.
Sections du résumé
BACKGROUND AND AIMS
OBJECTIVE
Artificial intelligence (AI)-based applications have transformed several industries and are widely used in various consumer products and services. In medicine, AI is primarily being used for image classification and natural language processing and has great potential to affect image-based specialties such as radiology, pathology, and gastroenterology (GE). This document reviews the reported applications of AI in GE, focusing on endoscopic image analysis.
METHODS
METHODS
The MEDLINE database was searched through May 2020 for relevant articles by using key words such as machine learning, deep learning, artificial intelligence, computer-aided diagnosis, convolutional neural networks, GI endoscopy, and endoscopic image analysis. References and citations of the retrieved articles were also evaluated to identify pertinent studies. The manuscript was drafted by 2 authors and reviewed in person by members of the American Society for Gastrointestinal Endoscopy Technology Committee and subsequently by the American Society for Gastrointestinal Endoscopy Governing Board.
RESULTS
RESULTS
Deep learning techniques such as convolutional neural networks have been used in several areas of GI endoscopy, including colorectal polyp detection and classification, analysis of endoscopic images for diagnosis of
CONCLUSIONS
CONCLUSIONS
The implementation of AI technologies across multiple GI endoscopic applications has the potential to transform clinical practice favorably and improve the efficiency and accuracy of current diagnostic methods.
Identifiants
pubmed: 33319126
doi: 10.1016/j.vgie.2020.08.013
pii: S2468-4481(20)30272-1
pmc: PMC7732722
doi:
Types de publication
Journal Article
Langues
eng
Pagination
598-613Informations de copyright
© 2020 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc.
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