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. World J Gastrointest Surg 2025; 17(1): 101772 [DOI: 10.4240/wjgs.v17.i1.101772]
Corresponding Author of This Article
Andrew A Gumbs, MD, MSc, FACS, 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
Research Domain of This Article
Surgery
Article-Type of This Article
Editorial
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 Gastrointest Surg. Jan 27, 2025; 17(1): 101772 Published online Jan 27, 2025. doi: 10.4240/wjgs.v17.i1.101772
Machine learning and deep learning to improve prevention of anastomotic leak after rectal cancer surgery
Francesco Celotto, Quoc R Bao, Giulia Capelli, Gaya Spolverato, Andrew A Gumbs
Francesco Celotto, Quoc R Bao, Gaya Spolverato, Department of Surgical, Oncological and Gastroenterological Sciences, University of Padova, Padova 35128, Veneto, Italy
Giulia Capelli, Department of Surgery, Azienda Socio Sanitaria Territoriale Bergamo Est, Bergamo 24068, Lombardy, Italy
Andrew A Gumbs, Department of Minimally Invasive Digestive Surgery, Antoine-Béclère Hospital, Assistance Publique-Hôpitaux de ParisClamart 92140, Haute-Seine, France
Andrew A Gumbs, Department of General, Visceral, Vascular and Transplant Surgery, University Hospital Magdeburg, Otto-Von-Guericke University, Magdeburg 39120, Sachsen-Anhalt, Germany
Co-first authors: Francesco Celotto and Quoc R Bao.
Co-corresponding authors: Gaya Spolverato and Andrew A Gumbs.
Author contributions: Gumbs AA and Spolverato G designed the overall concept and outline of the manuscript; Celotto F, Bao QR, and Capelli G contributed to the acquisition, analysis, data interpretation, and manuscript draft preparation; Celotto F, Bao QR, Capelli G, Spolverato G, and Gumbs A contributed to manuscript review and editing.
Conflict-of-interest statement: The authors have no conflicts of interest to declare.
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/
Corresponding author: Andrew A Gumbs, MD, MSc, FACS, 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
Received: September 25, 2024 Revised: October 30, 2024 Accepted: November 25, 2024 Published online: January 27, 2025 Processing time: 92 Days and 21.3 Hours
Core Tip
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 such as 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.
