Artificial intelligence for the detection of polyps or cancer with colon capsule endoscopy

Colorectal cancer is common and can be devastating, with long-term survival rates vastly improved by early diagnosis. Colon capsule endoscopy (CCE) is increasingly recognised as a reliable option for colonic surveillance, but widespread adoption has been slow for several reasons, including the time-...

Full description

Bibliographic Details
Authors: Robertson, Alexander R., Seguí Mesquida, Santi, Wenzek, Hagen, Koulaouzidis, Anastasios
Format: article
Status:Published version
Publication Date:2021
Country:España
Institution:Universidad de Barcelona
Repository:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/194822
Online Access:https://hdl.handle.net/2445/194822
Access Level:Open access
Keyword:Intel·ligència artificial
Pòlips (Patologia)
Càpsula endoscòpica
Càncer colorectal
Artificial intelligence
Polyps (Pathology)
Capsule endoscopy
Colorectal cancer
id ES_c773b94fcfbcfbda628076159fa03423
oai_identifier_str oai:diposit.ub.edu:2445/194822
network_acronym_str ES
network_name_str España
repository_id_str
spelling Artificial intelligence for the detection of polyps or cancer with colon capsule endoscopyRobertson, Alexander R.Seguí Mesquida, SantiWenzek, HagenKoulaouzidis, AnastasiosIntel·ligència artificialPòlips (Patologia)Càpsula endoscòpicaCàncer colorectalArtificial intelligencePolyps (Pathology)Capsule endoscopyColorectal cancerColorectal cancer is common and can be devastating, with long-term survival rates vastly improved by early diagnosis. Colon capsule endoscopy (CCE) is increasingly recognised as a reliable option for colonic surveillance, but widespread adoption has been slow for several reasons, including the time-consuming reading process of the CCE recording. Automated image recognition and artificial intelligence (AI) are appealing solutions in CCE. Through a review of the currently available and developmental technologies, we discuss how AI is poised to deliver at the forefront of CCE in the coming years. Current practice for CCE reporting often involves a two-step approach, with a 'pre-reader' and 'validator'. This requires skilled and experienced readers with a significant time commitment. Therefore, CCE is well-positioned to reap the benefits of the ongoing digital innovation. This is likely to initially involve an automated AI check of finished CCE evaluations as a quality control measure. Once felt reliable, AI could be used in conjunction with a 'pre-reader', before adopting more of this role by sending provisional results and abnormal frames to the validator. With time, AI would be able to evaluate the findings more thoroughly and reduce the input required from human readers and ultimately autogenerate a highly accurate report and recommendation of therapy, if required, for any pathology identified. As with many medical fields reliant on image recognition, AI will be a welcome aid in CCE. Initially, this will be as an adjunct to 'double-check' that nothing has been missed, but with time will hopefully lead to a faster, more convenient diagnostic service for the screening population.SAGE Publications2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/194822Articles publicats en revistes (Matemàtiques i Informàtica)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/10.1177/26317745211020277Therapeutic Advances in Gastroenterology, 2021, vol. 14, p. 1-8https://doi.org/10.1177/26317745211020277cc-by-nc (c) Robertson, Alexander R. et al., 2021https://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1948222026-05-27T06:46:51Z
dc.title.none.fl_str_mv Artificial intelligence for the detection of polyps or cancer with colon capsule endoscopy
title Artificial intelligence for the detection of polyps or cancer with colon capsule endoscopy
spellingShingle Artificial intelligence for the detection of polyps or cancer with colon capsule endoscopy
Robertson, Alexander R.
Intel·ligència artificial
Pòlips (Patologia)
Càpsula endoscòpica
Càncer colorectal
Artificial intelligence
Polyps (Pathology)
Capsule endoscopy
Colorectal cancer
title_short Artificial intelligence for the detection of polyps or cancer with colon capsule endoscopy
title_full Artificial intelligence for the detection of polyps or cancer with colon capsule endoscopy
title_fullStr Artificial intelligence for the detection of polyps or cancer with colon capsule endoscopy
title_full_unstemmed Artificial intelligence for the detection of polyps or cancer with colon capsule endoscopy
title_sort Artificial intelligence for the detection of polyps or cancer with colon capsule endoscopy
dc.creator.none.fl_str_mv Robertson, Alexander R.
Seguí Mesquida, Santi
Wenzek, Hagen
Koulaouzidis, Anastasios
author Robertson, Alexander R.
author_facet Robertson, Alexander R.
Seguí Mesquida, Santi
Wenzek, Hagen
Koulaouzidis, Anastasios
author_role author
author2 Seguí Mesquida, Santi
Wenzek, Hagen
Koulaouzidis, Anastasios
author2_role author
author
author
dc.subject.none.fl_str_mv Intel·ligència artificial
Pòlips (Patologia)
Càpsula endoscòpica
Càncer colorectal
Artificial intelligence
Polyps (Pathology)
Capsule endoscopy
Colorectal cancer
topic Intel·ligència artificial
Pòlips (Patologia)
Càpsula endoscòpica
Càncer colorectal
Artificial intelligence
Polyps (Pathology)
Capsule endoscopy
Colorectal cancer
description Colorectal cancer is common and can be devastating, with long-term survival rates vastly improved by early diagnosis. Colon capsule endoscopy (CCE) is increasingly recognised as a reliable option for colonic surveillance, but widespread adoption has been slow for several reasons, including the time-consuming reading process of the CCE recording. Automated image recognition and artificial intelligence (AI) are appealing solutions in CCE. Through a review of the currently available and developmental technologies, we discuss how AI is poised to deliver at the forefront of CCE in the coming years. Current practice for CCE reporting often involves a two-step approach, with a 'pre-reader' and 'validator'. This requires skilled and experienced readers with a significant time commitment. Therefore, CCE is well-positioned to reap the benefits of the ongoing digital innovation. This is likely to initially involve an automated AI check of finished CCE evaluations as a quality control measure. Once felt reliable, AI could be used in conjunction with a 'pre-reader', before adopting more of this role by sending provisional results and abnormal frames to the validator. With time, AI would be able to evaluate the findings more thoroughly and reduce the input required from human readers and ultimately autogenerate a highly accurate report and recommendation of therapy, if required, for any pathology identified. As with many medical fields reliant on image recognition, AI will be a welcome aid in CCE. Initially, this will be as an adjunct to 'double-check' that nothing has been missed, but with time will hopefully lead to a faster, more convenient diagnostic service for the screening population.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/194822
url https://hdl.handle.net/2445/194822
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1177/26317745211020277
Therapeutic Advances in Gastroenterology, 2021, vol. 14, p. 1-8
https://doi.org/10.1177/26317745211020277
dc.rights.none.fl_str_mv cc-by-nc (c) Robertson, Alexander R. et al., 2021
https://creativecommons.org/licenses/by-nc/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by-nc (c) Robertson, Alexander R. et al., 2021
https://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv SAGE Publications
publisher.none.fl_str_mv SAGE Publications
dc.source.none.fl_str_mv Articles publicats en revistes (Matemàtiques i Informàtica)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869419158494511104
score 15,301603

Similar Items