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- Publisher Website: 10.3748/wjg.v26.i35.5248
- Scopus: eid_2-s2.0-85092313437
- PMID: 32994685
- WOS: WOS:000574429300002
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Article: Is artificial intelligence the final answer to missed polyps in colonoscopy?
Title | Is artificial intelligence the final answer to missed polyps in colonoscopy? |
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Authors | |
Keywords | Artificial intelligence Adenoma Colonoscopy Colorectal cancer Polyps |
Issue Date | 2020 |
Publisher | Baishideng 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? |
Abstract | Lesions 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 Identifier | http://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 Field | Value | Language |
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dc.contributor.author | Lui, TKL | - |
dc.contributor.author | Leung, WK | - |
dc.date.accessioned | 2020-10-05T12:08:07Z | - |
dc.date.available | 2020-10-05T12:08:07Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | World Journal of Gastroenterology, 2020, v. 26 n. 35, p. 5248-5255 | - |
dc.identifier.issn | 1007-9327 | - |
dc.identifier.uri | http://hdl.handle.net/10722/288117 | - |
dc.description.abstract | Lesions 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.language | eng | - |
dc.publisher | Baishideng Publishing Group. The Journal's web site is located at http://www.wjgnet.com/1007-9327/index.htm | - |
dc.relation.ispartof | World Journal of Gastroenterology | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Artificial intelligence | - |
dc.subject | Adenoma | - |
dc.subject | Colonoscopy | - |
dc.subject | Colorectal cancer | - |
dc.subject | Polyps | - |
dc.title | Is artificial intelligence the final answer to missed polyps in colonoscopy? | - |
dc.type | Article | - |
dc.identifier.email | Leung, WK: waikleung@hku.hk | - |
dc.identifier.authority | Leung, WK=rp01479 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.3748/wjg.v26.i35.5248 | - |
dc.identifier.pmid | 32994685 | - |
dc.identifier.pmcid | PMC7504252 | - |
dc.identifier.scopus | eid_2-s2.0-85092313437 | - |
dc.identifier.hkuros | 315854 | - |
dc.identifier.volume | 26 | - |
dc.identifier.issue | 35 | - |
dc.identifier.spage | 5248 | - |
dc.identifier.epage | 5255 | - |
dc.identifier.isi | WOS:000574429300002 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1007-9327 | - |