Show simple item record

dc.contributor.authorKader, Rawen
dc.contributor.authorHadjinicolaou, Andreas V
dc.contributor.authorGeorgiades, Fanourios
dc.contributor.authorStoyanov, Danail
dc.contributor.authorLovat, Laurence B
dc.date.accessioned2021-11-17T11:53:30Z
dc.date.available2021-11-17T11:53:30Z
dc.date.issued2021-09-21
dc.identifier.issn1007-9327
dc.identifier.otherPMC8475008
dc.identifier.other34629808
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/330705
dc.description.abstractColonoscopy remains the gold standard investigation for colorectal cancer screening as it offers the opportunity to both detect and resect pre-malignant and neoplastic polyps. Although technologies for image-enhanced endoscopy are widely available, optical diagnosis has not been incorporated into routine clinical practice, mainly due to significant inter-operator variability. In recent years, there has been a growing number of studies demonstrating the potential of convolutional neural networks (CNN) to enhance optical diagnosis of polyps. Data suggest that the use of CNNs might mitigate the inter-operator variability amongst endoscopists, potentially enabling a "resect and discard" or "leave in" strategy to be adopted in real-time. This would have significant financial benefits for healthcare systems, avoid unnecessary polypectomies of non-neoplastic polyps and improve the efficiency of colonoscopy. Here, we review advances in CNN for the optical diagnosis of colorectal polyps, current limitations and future directions.
dc.languageeng
dc.publisherBaishideng Publishing Group Inc.
dc.rightsAttribution-NonCommercial 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.sourceessn: 2219-2840
dc.sourcenlmid: 100883448
dc.subjectArtificial intelligence
dc.subjectComputer aided diagnosis
dc.subjectColorectal Polyps
dc.subjectOptical Diagnosis
dc.subjectDeep Learning
dc.subjectConvolutional Neural Networks
dc.subjectHumans
dc.subjectColorectal Neoplasms
dc.subjectColonic Polyps
dc.subjectColonoscopy
dc.subjectEarly Detection of Cancer
dc.subjectNeural Networks, Computer
dc.titleOptical diagnosis of colorectal polyps using convolutional neural networks.
dc.typeArticle
dc.date.updated2021-11-17T11:51:57Z
prism.endingPage5918
prism.issueIdentifier35
prism.publicationNameWorld J Gastroenterol
prism.startingPage5908
prism.volume27
dc.identifier.doi10.17863/CAM.78149
dcterms.dateAccepted2021-08-24
rioxxterms.versionofrecord10.3748/wjg.v27.i35.5908
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.contributor.orcidGeorgiades, Fanourios [0000-0003-0440-2720]
dc.contributor.orcidLovat, Laurence B [0000-0003-4542-3915]
dc.identifier.eissn2219-2840
cam.issuedOnline2021-09-21


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial 4.0 International
Except where otherwise noted, this item's licence is described as Attribution-NonCommercial 4.0 International