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Articles Published Processes
9/15/2021 10:33:58 AM | Browse: 575 | Download: 1187
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Received |
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2021-02-27 21:26 |
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Peer-Review Started |
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2021-02-27 21:30 |
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To Make the First Decision |
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Return for Revision |
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2021-04-18 02:29 |
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Revised |
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2021-04-29 21:20 |
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Second Decision |
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2021-08-24 03:23 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2021-08-24 08:41 |
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Articles in Press |
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2021-08-24 08:41 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2021-09-13 01:01 |
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Publish the Manuscript Online |
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2021-09-15 10:33 |
| ISSN |
1007-9327 (print) and 2219-2840 (online) |
| Open Access |
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/ |
| Copyright |
© The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Minireviews |
| Article Title |
Optical diagnosis of colorectal polyps using convolutional neural networks
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Rawen Kader, Andreas V Hadjinicolaou, Fanourios Georgiades, Danail Stoyanov and Laurence B Lovat |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Rawen Kader, BMed, MBBS, MRCP, Research Fellow, Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, Charles Bell House, 43-45 Foley Street, Fitzrovia, London W1W 7TY, United Kingdom. r.kader@nhs.net |
| Key Words |
Artificial intelligence; Deep learning; Convolutional neural networks; Computer aided diagnosis; Optical diagnosis; Colorectal polyps |
| Core Tip |
A convolutional neural network (CNN) is a specific type of artificial intelligence deep learning. These networks may play an important role in the coming years in assisting endoscopists to optically diagnose colorectal polyps. CNNs can mitigate the inter-operator variability amongst endoscopists, potentially enabling a “resect and discard” or “leave in” strategy to be adopted. This would improve the efficiency of colonoscopy, reduce healthcare costs and reduce adverse events for patients by avoiding unnecessary resections of non-neoplastic polyps. In this article, we expand on the most relevant studies in this field and discuss limitations and future directions that will determine fulfilment of the potential of CNN in the optical diagnosis of colorectal polyps. |
| Publish Date |
2021-09-15 10:33 |
| Citation |
Kader R, Hadjinicolaou AV, Georgiades F, Stoyanov D, Lovat LB. Optical diagnosis of colorectal polyps using convolutional neural networks. World J Gastroenterol 2021; 27(35): 5908-5918 |
| URL |
https://www.wjgnet.com/1007-9327/full/v27/i35/5908.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v27.i35.5908 |
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