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 [PMID: 34629808 DOI: 10.3748/wjg.v27.i35.5908]
Corresponding Author of This Article
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
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
World J Gastroenterol. Sep 21, 2021; 27(35): 5908-5918 Published online Sep 21, 2021. doi: 10.3748/wjg.v27.i35.5908
Optical diagnosis of colorectal polyps using convolutional neural networks
Rawen Kader, Andreas V Hadjinicolaou, Fanourios Georgiades, Danail Stoyanov, Laurence B Lovat
Rawen Kader, Danail Stoyanov, Laurence B Lovat, Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, United Kingdom
Rawen Kader, Laurence B Lovat, Division of Surgery and Interventional Sciences, University College London, London W1W 7TY, United Kingdom
Andreas V Hadjinicolaou, MRC Cancer Unit, Department of Gastroenterology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
Fanourios Georgiades, Department of Surgery, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
Danail Stoyanov, Department of Computer Science, University College London, London W1W 7TY, United Kingdom
Author contributions: Kader R, Hadjinicolaou AV and Georgiades F performed the literature review and wrote the manuscript; Stoyanov D and Lovat LB revised the manuscript; All authors have read and approved the final manuscript.
Conflict-of-interest statement: Rawen Kader is supported by the Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) at UCL; [203145Z/16/Z]. Danail Stoyanov owns shares in Odin Vision and Digital Surgery Ltd. Laurence B Lovat owns shares in Odin Vision. The remaining authors declare no conflict of interest.
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/
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
Received: February 27, 2021 Peer-review started: February 27, 2021 First decision: April 18, 2021 Revised: April 29, 2021 Accepted: August 24, 2021 Article in press: August 24, 2021 Published online: September 21, 2021 Processing time: 199 Days and 13.1 Hours
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.
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.
