eprintid: 10135290 rev_number: 12 eprint_status: archive userid: 608 dir: disk0/10/13/52/90 datestamp: 2021-09-30 17:01:23 lastmod: 2021-09-30 17:01:23 status_changed: 2021-09-30 17:01:23 type: article metadata_visibility: show creators_name: Kader, R creators_name: Hadjinicolaou, AV creators_name: Georgiades, F creators_name: Stoyanov, D creators_name: Lovat, LB title: Optical diagnosis of colorectal polyps using convolutional neural networks ispublished: pub divisions: UCL divisions: B02 divisions: C10 divisions: D16 divisions: G88 divisions: B04 divisions: C05 divisions: F48 keywords: Artificial intelligence, Deep learning, Convolutional neural networks, Computer aided diagnosis, Optical diagnosis, Colorectal polyps note: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/ abstract: Colonoscopy 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. date: 2021-09-21 date_type: published official_url: http://dx.doi.org/10.3748/wjg.v27.i35.5908 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1889335 doi: 10.3748/wjg.v27.i35.5908 lyricists_name: Kader, Rawen lyricists_name: Lovat, Laurence lyricists_name: Stoyanov, Danail lyricists_id: RKADE07 lyricists_id: LBLOV52 lyricists_id: DSTOY26 actors_name: Kader, Rawen actors_id: RKADE07 actors_role: owner full_text_status: public publication: World Journal of Gastroenterology volume: 27 number: 35 pagerange: 5908-5918 citation: Kader, R; Hadjinicolaou, AV; Georgiades, F; Stoyanov, D; Lovat, LB; (2021) Optical diagnosis of colorectal polyps using convolutional neural networks. World Journal of Gastroenterology , 27 (35) pp. 5908-5918. 10.3748/wjg.v27.i35.5908 <https://doi.org/10.3748/wjg.v27.i35.5908>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10135290/1/WJG-27-5908.pdf