Overview of Deep Learning in Gastrointestinal Endoscopy

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 t...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Gut and liver Ročník 13; číslo 4; s. 388 - 393
Hlavní autori: Min, Jun Ki, Kwak, Min Seob, Cha, Jae Myung
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Korea (South) Editorial Office of Gut and Liver 11.01.2019
Gastroenterology Council for Gut and Liver
거트앤리버 소화기연관학회협의회
Predmet:
artificial intelligence
Capsule Endoscopy
Celiac Disease - diagnosis
Colonic Polyps - diagnosis
Colonoscopy
computer-assisted
convolutional neural network
Deep Learning
diagnosis
Diagnosis, Computer-Assisted
endoscopy
Endoscopy, Digestive System
Endoscopy, Gastrointestinal
Gastroenterology
Gastrointestinal Motility
Helicobacter Infections - diagnosis
Hookworm Infections - diagnosis
Humans
Intestinal Polyps - diagnosis
Review
Stomach Neoplasms - diagnosis
내과학
Deep learning
Diagnosis
Endoscopy
Artificial intelligence
Convolutional neural network
computer-assisted
ISSN:1976-2283, 2005-1212, 2005-1212
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
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, 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.
AbstractList 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 deeplearning 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. KCI Citation Count: 7
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.
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.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.
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, 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.
Author Kwak, Min Seob
Min, Jun Ki
Cha, Jae Myung
Author_xml – sequence: 1
  givenname: Jun Ki
  orcidid: 0000-0003-0354-855X
  surname: Min
  fullname: Min, Jun Ki
  organization: Department of Internal Medicine, Kyung Hee University School of Medicine, Seoul, Korea
– sequence: 2
  givenname: Min Seob
  orcidid: 0000-0002-8988-7423
  surname: Kwak
  fullname: Kwak, Min Seob
  organization: Department of Internal Medicine, Kyung Hee University School of Medicine, Seoul, Korea
– sequence: 3
  givenname: Jae Myung
  orcidid: 0000-0001-9403-230X
  surname: Cha
  fullname: Cha, Jae Myung
  organization: Department of Internal Medicine, Kyung Hee University School of Medicine, Seoul, Korea
BackLink https://www.ncbi.nlm.nih.gov/pubmed/30630221$$D View this record in MEDLINE/PubMed
https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002487569$$DAccess content in National Research Foundation of Korea (NRF)
BookMark eNplkk1rGzEQhkVJaZy00F9Q9tgeNtW3VpdCSNPUYAgU38WspHWVrCVXWrvk31ex47ShpwHNM88rpDlDJzFFj9B7gi8ExvrzKo6kYx1_hWYUY9ESSugJmhGtZEtpx07RWSl3GEtClXiDThmWDFNKZkjd7nzeBf-7SUPz1ftNs_CQY4irJsTmBsqUU4iTL1OIMDbX0aVi0-bhLXo9wFj8u6d6jpbfrpdX39vF7c386nLRWq7o1BIxgJfQeyodlbzvuZaaECuAEQceoKNywJjQnlgiHCeDII7XhrYWHLBz9OmgjXkw9zaYBGFfV8ncZ3P5Yzk3opOCalXZ-YF1Ce7MJoc15If9wP4g5ZWBPAU7emMHrBTj1vYeuJNMC62s4so6TZxVurq-HFybbb_2zvo4ZRhfSF92YvhZ77QzUlIqJK2Cj0-CnH5t6_OZdSjWjyNEn7bFUFJTGOeMVfTDv1nPIcdPqsDFAbA5lZL9YGyYYArpMTqMhmDzuAXmuAV_w58Hjs7_0D9z3bIN
CitedBy_id crossref_primary_10_3390_jcm9061648
crossref_primary_10_1016_j_tgie_2019_150642
crossref_primary_10_1097_MCG_0000000000001423
crossref_primary_10_3389_fmed_2021_781256
crossref_primary_10_1002_cncr_35768
crossref_primary_10_1007_s10354_023_01020_w
crossref_primary_10_3389_fonc_2021_673775
crossref_primary_10_1089_aipo_2024_0019
crossref_primary_10_1007_s00535_023_02001_x
crossref_primary_10_3390_cancers14194834
crossref_primary_10_1109_TIM_2020_3038011
crossref_primary_10_1016_j_gie_2019_11_012
crossref_primary_10_1016_j_imed_2021_12_001
crossref_primary_10_1016_j_diii_2022_10_001
crossref_primary_10_21015_vtse_v10i3_1054
crossref_primary_10_1002_lary_29960
crossref_primary_10_4166_kjg_2024_126
crossref_primary_10_1186_s12935_025_03756_4
crossref_primary_10_1016_j_csbj_2020_08_003
crossref_primary_10_3390_s22239250
crossref_primary_10_1007_s12565_022_00681_8
crossref_primary_10_1049_htl2_12049
crossref_primary_10_3390_bioengineering9110632
crossref_primary_10_3748_wjg_v29_i5_879
crossref_primary_10_1177_00045632231154782
crossref_primary_10_1016_j_isci_2023_107463
crossref_primary_10_1093_ibd_izaa211
crossref_primary_10_3390_bioengineering12040413
crossref_primary_10_3390_info15010053
crossref_primary_10_1109_ACCESS_2020_3047544
crossref_primary_10_1016_j_gie_2021_03_013
crossref_primary_10_1186_s12880_023_01098_z
crossref_primary_10_1016_j_iswa_2022_200149
crossref_primary_10_1093_ibd_izac226
crossref_primary_10_3390_biomimetics7020079
crossref_primary_10_1016_j_device_2025_100867
crossref_primary_10_4103_jpi_jpi_68_19
crossref_primary_10_3390_diagnostics13061038
crossref_primary_10_1007_s11042_024_19694_8
crossref_primary_10_1111_den_13769
crossref_primary_10_3390_jimaging11070243
crossref_primary_10_1055_a_1013_6593
crossref_primary_10_1002_lipd_12352
crossref_primary_10_1097_DCR_0000000000002295
crossref_primary_10_1038_s41598_021_93202_y
crossref_primary_10_1063_5_0100192
crossref_primary_10_1038_s41598_022_25618_z
crossref_primary_10_1136_bmjopen_2020_041139
crossref_primary_10_3748_wjg_v26_i35_5256
crossref_primary_10_1016_j_compbiomed_2024_109030
crossref_primary_10_1016_j_isci_2025_113045
crossref_primary_10_3748_wjg_v26_i37_5606
crossref_primary_10_1080_08820538_2021_1893763
crossref_primary_10_1016_j_bspc_2022_104504
crossref_primary_10_1093_ajcp_aqaa001
crossref_primary_10_1007_s00371_021_02322_z
crossref_primary_10_1016_j_jds_2023_04_022
crossref_primary_10_4103_sjg_sjg_391_22
crossref_primary_10_1007_s11042_025_20627_2
crossref_primary_10_3748_wjg_v31_i36_111137
crossref_primary_10_4103_ijh_ijh_110_24
crossref_primary_10_1109_ACCESS_2020_2981765
crossref_primary_10_1007_s10151_022_02602_3
crossref_primary_10_3748_wjg_v29_i22_3561
crossref_primary_10_1080_17474124_2022_2020646
crossref_primary_10_1016_j_artmed_2022_102435
crossref_primary_10_4251_wjgo_v14_i1_124
crossref_primary_10_3233_THC_248004
crossref_primary_10_1007_s00464_022_09378_5
crossref_primary_10_1007_s00464_020_08150_x
crossref_primary_10_3390_cancers15215126
crossref_primary_10_1016_j_gie_2021_12_020
crossref_primary_10_3390_electronics14101913
crossref_primary_10_7759_cureus_79472
crossref_primary_10_1007_s11042_022_12258_8
crossref_primary_10_1245_s10434_024_15311_y
crossref_primary_10_3390_biomedicines11123171
crossref_primary_10_3390_diagnostics11091672
crossref_primary_10_1177_26317745241306562
crossref_primary_10_1038_s41598_024_84737_x
crossref_primary_10_1038_s41598_022_05001_8
crossref_primary_10_3390_diagnostics15162009
crossref_primary_10_1038_s41598_022_08773_1
crossref_primary_10_3390_app14188115
crossref_primary_10_1016_j_neucom_2020_02_123
crossref_primary_10_3389_fmolb_2021_614277
crossref_primary_10_3390_diagnostics11091719
crossref_primary_10_1049_htl_2019_0066
crossref_primary_10_1186_s12911_020_01314_8
crossref_primary_10_1038_s41598_020_78556_z
crossref_primary_10_1038_s41598_022_26372_y
crossref_primary_10_1016_j_jcpo_2021_100295
crossref_primary_10_3390_diagnostics11071192
crossref_primary_10_1016_j_gie_2020_04_074
crossref_primary_10_3390_app10238501
crossref_primary_10_1016_j_media_2023_102802
crossref_primary_10_1007_s10489_022_04146_3
crossref_primary_10_1007_s00521_024_10287_y
crossref_primary_10_1016_j_canlet_2023_216238
crossref_primary_10_4103_sjg_SJG_636_19
crossref_primary_10_1007_s11042_022_14177_0
crossref_primary_10_1136_bmjgast_2019_000371
crossref_primary_10_1186_s12876_023_03067_w
crossref_primary_10_1038_s43856_025_01030_2
crossref_primary_10_3748_wjg_v31_i19_104897
crossref_primary_10_1177_03000605231200371
crossref_primary_10_1109_ACCESS_2023_3319068
crossref_primary_10_3389_fonc_2022_1001840
Cites_doi 10.1016/j.ebiom.2017.10.014
10.1001/jama.2017.18152
10.1038/srep46450
10.1136/gutjnl-2017-314547
10.1109/JBHI.2016.2635662
10.1055/s-0043-120830
10.1148/radiol.2017171928
10.1007/s10120-018-0793-2
10.1371/journal.pone.0171472
10.1038/s41598-017-05728-9
10.1159/000481227
10.1016/j.compbiomed.2016.10.011
10.1109/TIP.2018.2801119
10.1109/JBHI.2016.2637004
10.1038/s41598-018-25842-6
10.1109/TMI.2016.2527736
10.1148/radiol.2017170236
10.1001/jama.2016.17216
10.1016/j.compbiomed.2017.03.031
10.1038/nature21056
10.1016/j.canlet.2016.05.033
10.3348/kjr.2017.18.4.570
10.1148/radiol.2017170706
10.1001/jama.2017.14585
ContentType Journal Article
Copyright Copyright © 2019 by The Korean Society of Gastroenterology, the Korean Society of Gastrointestinal Endoscopy, the Korean Society of Neurogastroenterology and Motility, Korean College of Helicobacter and Upper Gastrointestinal Research, Korean Association the Study of Intestinal Diseases, the Korean Association for the Study of the Liver, Korean Pancreatobiliary Association, and Korean Society of Gastrointestinal Cancer. 2019
Copyright_xml – notice: Copyright © 2019 by The Korean Society of Gastroenterology, the Korean Society of Gastrointestinal Endoscopy, the Korean Society of Neurogastroenterology and Motility, Korean College of Helicobacter and Upper Gastrointestinal Research, Korean Association the Study of Intestinal Diseases, the Korean Association for the Study of the Liver, Korean Pancreatobiliary Association, and Korean Society of Gastrointestinal Cancer. 2019
DBID AAYXX
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
7X8
5PM
DOA
ACYCR
DOI 10.5009/gnl18384
DatabaseName CrossRef
Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
MEDLINE - Academic
PubMed Central (Full Participant titles)
DOAJ Directory of Open Access Journals
Korean Citation Index
DatabaseTitle CrossRef
MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
MEDLINE - Academic
DatabaseTitleList

MEDLINE - Academic

MEDLINE
Database_xml – sequence: 1
  dbid: DOA
  name: Directory of Open Access Journals (DOAJ)
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: NPM
  name: PubMed
  url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 3
  dbid: 7X8
  name: MEDLINE - Academic
  url: https://search.proquest.com/medline
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Medicine
EISSN 2005-1212
EndPage 393
ExternalDocumentID oai_kci_go_kr_ARTI_5865297
oai_doaj_org_article_cf07734ccbea4d639597c747cd91dc79
PMC6622562
30630221
10_5009_gnl18384
Genre Journal Article
Review
GroupedDBID ---
5-W
8JR
AAKDD
AAYXX
ABDBF
ACUHS
ACYCR
ADBBV
AENEX
ALMA_UNASSIGNED_HOLDINGS
AOIJS
BAWUL
CITATION
DIK
E3Z
EBD
EF.
F5P
GROUPED_DOAJ
GX1
HYE
HZB
OK1
RPM
9ZL
CGR
CUY
CVF
ECM
EIF
M~E
NPM
7X8
5PM
53G
85H
ID FETCH-LOGICAL-c472t-15fae6abe26d264bb496911c5a31daeaa826f0012b1c15d41f51d4aea9ccada3
IEDL.DBID DOA
ISICitedReferencesCount 131
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000474632100004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 1976-2283
2005-1212
IngestDate Tue Nov 21 21:40:35 EST 2023
Fri Oct 03 12:50:39 EDT 2025
Tue Nov 04 01:58:14 EST 2025
Thu Jul 10 18:07:02 EDT 2025
Thu Jan 02 22:59:16 EST 2025
Tue Nov 18 20:47:48 EST 2025
Sat Nov 29 01:46:41 EST 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 4
Keywords Deep learning
Diagnosis
Endoscopy
Artificial intelligence
Convolutional neural network
computer-assisted
Language English
License This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c472t-15fae6abe26d264bb496911c5a31daeaa826f0012b1c15d41f51d4aea9ccada3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
ObjectType-Review-3
content type line 23
ORCID 0000-0002-8988-7423
0000-0001-9403-230X
0000-0003-0354-855X
OpenAccessLink https://doaj.org/article/cf07734ccbea4d639597c747cd91dc79
PMID 30630221
PQID 2179334433
PQPubID 23479
PageCount 6
ParticipantIDs nrf_kci_oai_kci_go_kr_ARTI_5865297
doaj_primary_oai_doaj_org_article_cf07734ccbea4d639597c747cd91dc79
pubmedcentral_primary_oai_pubmedcentral_nih_gov_6622562
proquest_miscellaneous_2179334433
pubmed_primary_30630221
crossref_citationtrail_10_5009_gnl18384
crossref_primary_10_5009_gnl18384
PublicationCentury 2000
PublicationDate 20190111
PublicationDateYYYYMMDD 2019-01-11
PublicationDate_xml – month: 1
  year: 2019
  text: 20190111
  day: 11
PublicationDecade 2010
PublicationPlace Korea (South)
PublicationPlace_xml – name: Korea (South)
PublicationTitle Gut and liver
PublicationTitleAlternate Gut Liver
PublicationYear 2019
Publisher Editorial Office of Gut and Liver
Gastroenterology Council for Gut and Liver
거트앤리버 소화기연관학회협의회
Publisher_xml – name: Editorial Office of Gut and Liver
– name: Gastroenterology Council for Gut and Liver
– name: 거트앤리버 소화기연관학회협의회
References ref13
ref12
ref15
ref14
ref30
ref11
ref10
ref2
ref1
ref17
ref16
ref19
ref18
ref24
ref23
ref26
ref25
ref20
ref22
ref21
ref28
ref27
ref29
ref8
ref7
ref9
ref4
ref3
ref6
ref5
References_xml – ident: ref21
  doi: 10.1016/j.ebiom.2017.10.014
– ident: ref17
  doi: 10.1001/jama.2017.18152
– ident: ref1
– ident: ref3
– ident: ref7
– ident: ref12
  doi: 10.1038/srep46450
– ident: ref24
  doi: 10.1136/gutjnl-2017-314547
– ident: ref25
  doi: 10.1109/JBHI.2016.2635662
– ident: ref20
  doi: 10.1055/s-0043-120830
– ident: ref10
  doi: 10.1148/radiol.2017171928
– ident: ref22
  doi: 10.1007/s10120-018-0793-2
– ident: ref19
– ident: ref2
– ident: ref5
  doi: 10.1371/journal.pone.0171472
– ident: ref6
– ident: ref13
  doi: 10.1038/s41598-017-05728-9
– ident: ref23
  doi: 10.1159/000481227
– ident: ref29
  doi: 10.1016/j.compbiomed.2016.10.011
– ident: ref28
  doi: 10.1109/TIP.2018.2801119
– ident: ref26
  doi: 10.1109/JBHI.2016.2637004
– ident: ref8
  doi: 10.1038/s41598-018-25842-6
– ident: ref30
  doi: 10.1109/TMI.2016.2527736
– ident: ref9
  doi: 10.1148/radiol.2017170236
– ident: ref16
  doi: 10.1001/jama.2016.17216
– ident: ref27
  doi: 10.1016/j.compbiomed.2017.03.031
– ident: ref15
  doi: 10.1038/nature21056
– ident: ref14
  doi: 10.1016/j.canlet.2016.05.033
– ident: ref4
  doi: 10.3348/kjr.2017.18.4.570
– ident: ref11
  doi: 10.1148/radiol.2017170706
– ident: ref18
  doi: 10.1001/jama.2017.14585
SSID ssj0061275
Score 2.5337222
SecondaryResourceType review_article
Snippet Artificial intelligence is likely to perform several roles currently performed by humans, and the adoption of artificial intelligence-based medicine in...
SourceID nrf
doaj
pubmedcentral
proquest
pubmed
crossref
SourceType Open Website
Open Access Repository
Aggregation Database
Index Database
Enrichment Source
StartPage 388
SubjectTerms artificial intelligence
Capsule Endoscopy
Celiac Disease - diagnosis
Colonic Polyps - diagnosis
Colonoscopy
computer-assisted
convolutional neural network
Deep Learning
diagnosis
Diagnosis, Computer-Assisted
endoscopy
Endoscopy, Digestive System
Endoscopy, Gastrointestinal
Gastroenterology
Gastrointestinal Motility
Helicobacter Infections - diagnosis
Hookworm Infections - diagnosis
Humans
Intestinal Polyps - diagnosis
Review
Stomach Neoplasms - diagnosis
내과학
Title Overview of Deep Learning in Gastrointestinal Endoscopy
URI https://www.ncbi.nlm.nih.gov/pubmed/30630221
https://www.proquest.com/docview/2179334433
https://pubmed.ncbi.nlm.nih.gov/PMC6622562
https://doaj.org/article/cf07734ccbea4d639597c747cd91dc79
https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002487569
Volume 13
WOSCitedRecordID wos000474632100004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
ispartofPNX Gut and Liver, 2019, 13(4), , pp.388-393
journalDatabaseRights – providerCode: PRVAON
  databaseName: Directory of Open Access Journals (DOAJ)
  customDbUrl:
  eissn: 2005-1212
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0061275
  issn: 1976-2283
  databaseCode: DOA
  dateStart: 20070101
  isFulltext: true
  titleUrlDefault: https://www.doaj.org/
  providerName: Directory of Open Access Journals
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELagQogLKs8GShUQEier8Ts-ttACBwqHHvZm-bmsWjlVdlvEv2ecZFddVIkLp0ixEznzxTPfyPY3CL3XWlnSRIZ91A3m0bfYCesxTSKkwEiSzg7FJtTZWTub6R-3Sn2VPWGjPPBouEOfGqUY995FywPEU2DAHjiwD5oEr4aje43S62Rq9MGyyJYP68lK4iLwMsrOCiAUh_N8Cb9xy7cC0aDXD-El9-kuqvn3jslbIeh0Fz2euGN9NI75CboX81P08Nu0Ov4Mqe83ZebHX3WX6k8xXtWTeuq8XuT6s12u-q7IQ8CsLu85yaErh1J-P0fnpyfnH7_gqTAC9lzRFSYi2Siti1QGIDTOcS3BaXlhGQk2Wgs5QypExhFPROAkCRI4NGjAK1j2Au3kLsc9VPPkFY2S61Yk3lqpbQBKkkRqbNM44ir0YW0k4yfR8FK74tJA8lDMadbmrNDbTc-rUSjjjj7Hxc6b9iJtPdwAwM0EuPkX4BV6ByiZC78Yni_XeWcuegMJwFcjWimoVjCaNYgG5ktZBLE5dtdLQ4tHYpwzVqGXI6ib8bAiQEYpqZDagntrwNstefFz0OSWEhyjpK_-xxe-Ro-AlpVtbJiQfbSz6q_jG_TA36wWy_4A3Vez9mD43f8AsTQFcg
linkProvider Directory of Open Access Journals
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Overview+of+Deep+Learning+in+Gastrointestinal+Endoscopy&rft.jtitle=Gut+and+liver&rft.au=Jun+Ki+Min&rft.au=Min+Seob+Kwak&rft.au=%EC%B0%A8%EC%9E%AC%EB%AA%85&rft.date=2019-01-11&rft.pub=%EA%B1%B0%ED%8A%B8%EC%95%A4%EB%A6%AC%EB%B2%84+%EC%86%8C%ED%99%94%EA%B8%B0%EC%97%B0%EA%B4%80%ED%95%99%ED%9A%8C%ED%98%91%EC%9D%98%ED%9A%8C&rft.issn=1976-2283&rft.eissn=2005-1212&rft.spage=388&rft.epage=393&rft_id=info:doi/10.5009%2Fgnl18384&rft.externalDBID=n%2Fa&rft.externalDocID=oai_kci_go_kr_ARTI_5865297
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1976-2283&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1976-2283&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1976-2283&client=summon