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Articles Published Processes
12/27/2024 1:37:17 PM | Browse: 35 | Download: 121
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Received |
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2024-09-25 16:30 |
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Peer-Review Started |
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2024-09-25 16:30 |
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To Make the First Decision |
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Return for Revision |
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2024-10-23 11:13 |
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Revised |
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2024-10-30 23:19 |
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Second Decision |
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2024-11-25 02:34 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2024-11-25 07:23 |
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Articles in Press |
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2024-11-25 07:23 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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2024-12-01 23:49 |
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Typeset the Manuscript |
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2024-12-09 00:45 |
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Publish the Manuscript Online |
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2024-12-27 13:37 |
| ISSN |
1948-9366 (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: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2024. 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 |
Surgery |
| Manuscript Type |
Editorial |
| Article Title |
Machine learning and deep learning to improve prevention of anastomotic leak after rectal cancer surgery
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Francesco Celotto, Quoc R Bao, Giulia Capelli, Gaya Spolverato and Andrew A Gumbs |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Andrew A Gumbs, MD, Department of Minimally Invasive Digestive Surgery, Antoine-Béclère Hospital, Assistance Publique-Hôpitaux de Paris, 157 Rue de la Porte de Trivaux, Clamart 92140, Haute-Seine, France. aagumbs@gmail.com |
| Key Words |
Anastomotic leak; Rectal cancer; Surgery; Machine learning; Deep Learning |
| Core Tip |
Anastomotic leak (AL) is a major postoperative complication following rectal cancer surgery, significantly impacting patient quality of life and delaying cancer treatments. Machine learning and deep learning are revolutionizing the management of AL by identifying risk factors such as malnutrition, visceral fat, and tumor characteristics. Artificial intelligence (AI)-driven models outperform traditional statistical approaches in predicting AL and guiding surgical decisions, including the necessity of temporary stomas. Additionally, AI enhances intraoperative techniques like real-time blood perfusion monitoring through indocyanine green angiography and image segmentation. These advances enable more personalized care, improving patient outcomes in rectal cancer treatment. |
| Publish Date |
2024-12-27 13:37 |
| Citation |
<p>Celotto F, Bao QR, Capelli G, Spolverato G, Gumbs AA. Machine learning and deep learning to improve prevention of anastomotic leak after rectal cancer surgery. <i>World J Gastrointest Surg</i> 2025; 17(1): 101772</p> |
| URL |
https://www.wjgnet.com/1948-9366/full/v17/i1/101772.htm |
| DOI |
https://dx.doi.org/10.4240/wjgs.v17.i1.101772 |
© 2004-2025 Baishideng Publishing Group Inc. All rights reserved. 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
California Corporate Number: 3537345
