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7/9/2024 6:33:41 AM | Browse: 31 | Download: 0
Publication Name World Journal of Gastrointestinal Surgery
Manuscript ID 96526
Country Italy
Category Surgery
Manuscript Type Editorial
Article Title Advancing perioperative optimization in Crohn's disease surgery with machine learning predictions
Manuscript Source Invited Manuscript
All Author List Olga Maria Nardone, Fabiana Castiglione and Simone Maurea
Funding Agency and Grant Number
Corresponding Author Olga Maria Nardone, MD, PhD, Assistant Professor, Department of Public Health, University of Naples Federico II, Via Pansini 5, Naples 80131, Italy. olgamaria.nardone@unina.it
Key Words Crohn's disease; Surgery; Postoperative complications; Ileocecal resection; Machine learning
Core Tip Preoperative optimization for patients with Crohn's disease presents numerous challenges. Traditional research methods are insufficient to address the expanding array of questions in this field. Hence, there is a growing need to explore and develop new tools grounded in advanced technologies, such as computer simulation models and artificial intelligence. Employing machine learning approaches can significantly enhance patient risk stratification and the formulation of preoperative optimization strategies, ultimately aiming to improve post-surgical outcomes.
Citation Nardone OM, Castiglione F, Maurea S. Advancing perioperative optimization in Crohn's disease surgery with machine learning predictions. World J Gastrointest Surg 2024; In press
Received
2024-05-08 23:03
Peer-Review Started
2024-05-08 23:03
To Make the First Decision
Return for Revision
2024-06-19 06:14
Revised
2024-06-28 10:21
Second Decision
2024-07-09 02:42
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief
2024-07-09 06:33
Articles in Press
2024-07-09 06:33
Publication Fee Transferred
Edit the Manuscript by Language Editor
2024-07-10 14:54
Typeset the Manuscript
2024-07-17 00:28
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.
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