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Article: Is artificial intelligence the final answer to missed polyps in colonoscopy?

TitleIs artificial intelligence the final answer to missed polyps in colonoscopy?
Authors
KeywordsArtificial intelligence
Adenoma
Colonoscopy
Colorectal cancer
Polyps
Issue Date2020
PublisherBaishideng Publishing Group. The Journal's web site is located at http://www.wjgnet.com/1007-9327/index.htm
Citation
World Journal of Gastroenterology, 2020, v. 26 n. 35, p. 5248-5255 How to Cite?
AbstractLesions missed by colonoscopy are one of the main reasons for post-colonoscopy colorectal cancer, which is usually associated with a worse prognosis. Because the adenoma miss rate could be as high as 26%, it has been noted that endoscopists with higher adenoma detection rates are usually associated with lower adenoma miss rates. Artificial intelligence (AI), particularly the deep learning model, is a promising innovation in colonoscopy. Recent studies have shown that AI is not only accurate in colorectal polyp detection but can also reduce the miss rate. Nevertheless, the application of AI in real-time detection has been hindered by heterogeneity of the AI models and study design as well as a lack of long-term outcomes. Herein, we discussed the principle of various AI models and systematically reviewed the current data on the use of AI on colorectal polyp detection and miss rates. The limitations and future prospects of AI on colorectal polyp detection are also discussed.
Persistent Identifierhttp://hdl.handle.net/10722/288117
ISSN
2023 Impact Factor: 4.3
2023 SCImago Journal Rankings: 1.063
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLui, TKL-
dc.contributor.authorLeung, WK-
dc.date.accessioned2020-10-05T12:08:07Z-
dc.date.available2020-10-05T12:08:07Z-
dc.date.issued2020-
dc.identifier.citationWorld Journal of Gastroenterology, 2020, v. 26 n. 35, p. 5248-5255-
dc.identifier.issn1007-9327-
dc.identifier.urihttp://hdl.handle.net/10722/288117-
dc.description.abstractLesions missed by colonoscopy are one of the main reasons for post-colonoscopy colorectal cancer, which is usually associated with a worse prognosis. Because the adenoma miss rate could be as high as 26%, it has been noted that endoscopists with higher adenoma detection rates are usually associated with lower adenoma miss rates. Artificial intelligence (AI), particularly the deep learning model, is a promising innovation in colonoscopy. Recent studies have shown that AI is not only accurate in colorectal polyp detection but can also reduce the miss rate. Nevertheless, the application of AI in real-time detection has been hindered by heterogeneity of the AI models and study design as well as a lack of long-term outcomes. Herein, we discussed the principle of various AI models and systematically reviewed the current data on the use of AI on colorectal polyp detection and miss rates. The limitations and future prospects of AI on colorectal polyp detection are also discussed.-
dc.languageeng-
dc.publisherBaishideng Publishing Group. The Journal's web site is located at http://www.wjgnet.com/1007-9327/index.htm-
dc.relation.ispartofWorld Journal of Gastroenterology-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectArtificial intelligence-
dc.subjectAdenoma-
dc.subjectColonoscopy-
dc.subjectColorectal cancer-
dc.subjectPolyps-
dc.titleIs artificial intelligence the final answer to missed polyps in colonoscopy?-
dc.typeArticle-
dc.identifier.emailLeung, WK: waikleung@hku.hk-
dc.identifier.authorityLeung, WK=rp01479-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.3748/wjg.v26.i35.5248-
dc.identifier.pmid32994685-
dc.identifier.pmcidPMC7504252-
dc.identifier.scopuseid_2-s2.0-85092313437-
dc.identifier.hkuros315854-
dc.identifier.volume26-
dc.identifier.issue35-
dc.identifier.spage5248-
dc.identifier.epage5255-
dc.identifier.isiWOS:000574429300002-
dc.publisher.placeUnited States-
dc.identifier.issnl1007-9327-

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